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Добірка наукової літератури з теми "YOLOv8"

Автор: Grafiati

Опубліковано: 11 січня 2025

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Статті в журналах з теми "YOLOv8"

Sharma, Pravek, Dr Rajesh Tyagi, and Dr Priyanka Dubey. "Optimizing Real-Time Object Detection- A Comparison of YOLO Models." International Journal of Innovative Research in Computer Science and Technology 12, no. 3 (May 2024): 57–74. http://dx.doi.org/10.55524/ijircst.2024.12.3.11.

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Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.

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Tahir, Noor Ul Ain, Zhe Long, Zuping Zhang, Muhammad Asim, and Mohammed ELAffendi. "PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8." Drones 8, no. 3 (February 28, 2024): 84. http://dx.doi.org/10.3390/drones8030084.

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In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.

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Wulanningrum, Resty, Anik Nur Handayani, and Aji Prasetya Wibawa. "Perbandingan Instance Segmentation Image Pada Yolo8." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (August 22, 2024): 753–60. http://dx.doi.org/10.25126/jtiik.1148288.

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Seorang pejalan kaki sangat rawan terhadap kecelakaan di jalan. Deteksi pejalan kaki merupakan salah satu cara untuk mengidentifikasi atau megklasifikasikan antara orang, jalan atau yang lainnya. Instance segmentation adalah salah satu proses untuk melakukan segmentasi antara orang dan jalan. Instance segmentation dan penggunaan yolov8 merupakan salah satu implementasi dalam deteksi pejalan kaki. Perbandingan segmentasi pada dataset Penn-Fundan Database menggunakan yolov8 dengan model yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg. Penelitian ini menggunakan dataset publik pedestrian atau pejalan kaki dengan objek multi person yang diambil dari dataset Penn-Fudan Database. Dataset mempunyai 2 kelas, yaitu orang dan jalan. Hasil perbandingan penggunaan model yolov8 model segmentasi yang terbaik adalah menggunakan model yolov8l-seg. Hasil penelitian didapatkan Instance segmentation valid box pada data orang, mAP50 tertinggi pada yolov8l-seg dengan nilai 0,828 dan mAP50-95 adalah 0,723. Instance segmentation valid mask pada orang nilai mAP50 tertinggi pada yolov8l-seg dengan nilai 0,825 dan mAP50-95 adalah 0,645. Pada penelitian ini, yolov8l-seg menjadi nilai terbaik dibandingkan versi yang lain, karena berdasarkan nilai mAP tertinggi pada valid mask sebesar 0,825. Abstract A pedestrian is very vulnerable to road accidents. Pedestrian detection is one way to identify or classify between people, roads or others. Instance segmentation is one of the processes to segment people and roads. Instance segmentation and the use of yolov8 is one of the implementations in pedestrian detection. Comparison of segmentation on Penn-Fundan Database dataset using yolov8 with yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg models. This research uses a public pedestrian dataset with multi-person objects taken from the Penn-Fudan Database dataset. The dataset has 2 classes, namely people and roads. The results of the comparison using the yolov8 model, the best segmentation model is using the yolov8l-seg model. The results obtained Instance segmentation valid box on people data, the highest mAP50 on yolov8l-seg with a value of 0.828 and mAP50-95 is 0.723. Instance segmentation valid mask on people the highest mAP50 value on yolov8l-seg with a value of 0.825 and mAP50-95 is 0.645. In his study, yolov8l-seg is the best value compared to other versions, because based on the highest mAP value on the valid mask of 0.825.

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Panja, Eben, Hendry Hendry, and Christine Dewi. "YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions." Scientific Journal of Informatics 11, no. 1 (February 28, 2024): 127–38. http://dx.doi.org/10.15294/sji.v11i1.49038.

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Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.

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Podder, Soumyajit, Abhishek Mallick, Sudipta Das, Kartik Sau, and Arijit Roy. "Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images." AIMS Biophysics 10, no. 4 (2023): 453–81. http://dx.doi.org/10.3934/biophy.2023026.

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<abstract> <p>Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones.</p> </abstract>

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Liu, Yinzeng, Fandi Zeng, Hongwei Diao, Junke Zhu, Dong Ji, Xijie Liao, and Zhihuan Zhao. "YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion." Sensors 24, no. 13 (July 5, 2024): 4379. http://dx.doi.org/10.3390/s24134379.

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Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

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Sun, Daozong, Kai Zhang, Hongsheng Zhong, Jiaxing Xie, Xiuyun Xue, Mali Yan, Weibin Wu, and Jiehao Li. "Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model." Agriculture 14, no. 3 (February 22, 2024): 353. http://dx.doi.org/10.3390/agriculture14030353.

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Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model’s ability to extract features. The backbone network’s C2f model is replaced with the VoV-GSCSP module to reduce the model’s computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification.

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Çakmakçı, Cihan. "Dijital Hayvancılıkta Yapay Zekâ ve İnsansız Hava Araçları: Derin Öğrenme ve Bilgisayarlı Görme İle Dağlık ve Engebeli Arazide Kıl Keçisi Tespiti, Takibi ve Sayımı." Turkish Journal of Agriculture - Food Science and Technology 12, no. 7 (July 14, 2024): 1162–73. http://dx.doi.org/10.24925/turjaf.v12i7.1162-1173.6701.

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Küresel gıda talebindeki hızlı artış nedeniyle yüksek kaliteli hayvansal ürün üretiminin artırılması gerekliliği, modern hayvancılık uygulamalarında teknoloji kullanımı ihtiyacını beraberinde getirmiştir. Özellikle ekstansif koşullarda küçükbaş hayvan yetiştiriciliğinde hayvanların otomatik olarak izlenmesi ve yönetilmesi, verimliliğin artırılması açısından büyük öneme sahiptir. Bu noktada, insansız hava araçlarından elde edilen yüksek çözünürlüklü görüntüler ile derin öğrenme algoritmalarının birleştirilmesi, sürülerin uzaktan takip edilmesinde etkili çözümler sunma potansiyeli taşımaktadır. Bu çalışmada, insansız hava araçlarından (İHA) elde edilen yüksek çözünürlüklü görüntüler üzerinde derin öğrenme algoritmaları kullanılarak kıl keçilerinin otomatik olarak tespit edilmesi, takip edilmesi ve sayılması amaçlanmıştır. Bu kapsamda, en güncel You Only Look Once (YOLOv8) mimari varyasyonlarından YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l ve YOLOv8x olmak üzere beş farklı model gerçek hayvan izleme uçuşlarından elde edilen İHA görüntüleri üzerinde eğitilmiştir. Elde edilen bulgulara göre, 0,95 F1 skoru ve 0,99 mAP50 değeri ile hem sınırlayıcı kutu tespiti hem de segmentasyon performansı açısından en yüksek başarımı YOLOv8s mimarisi göstermiştir. Sonuç olarak, önerilen derin öğrenme tabanlı yaklaşımın, İHA destekli hassas hayvancılık uygulamalarında etkili, düşük maliyetli ve sürdürülebilir bir çözüm olabileceği öngörülmektedir.

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Arini Parhusip, Hanna, Suryasatriya Trihandaru, Denny Indrajaya, and Jane Labadin. "Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (September 1, 2024): 3291. http://dx.doi.org/10.11591/ijai.v13.i3.pp3291-3305.

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<p>You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.</p>

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Salma, Kartika, and Syarif Hidayat. "Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 5, no. 2 (May 10, 2024): 1611–18. http://dx.doi.org/10.35870/jimik.v5i2.716.

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The implementation of Face Emotion Recognition (FER) technology in online classes opens up new opportunities to effectively monitor students' emotional responses and adjust the teaching approach. Through FER, instructors can monitor students' emotional responses to learning materials in real-time and enable quick adjustments based on individual needs. Additionally, this technology can also be used to detect the level of enthusiasm or lack thereof among students towards the learning process, allowing for the optimization of teaching strategies. This study focuses on the implementation of the YOLOv8 algorithm in detecting students' enthusiasm, comparing the performance of YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l models. Test results show that YOLOv8n performs the best with an accuracy rate of 95.3% and a fast inference time of 62ms, enabling real-time object detection. Thus, the application of YOLOv8 in this context aims to detect students' enthusiasm in real-time and allows instructors to quickly adjust their approach to meet students' needs. Furthermore, this research contributes to improving the quality of online learning by providing insights into students' emotional engagement and serving as a tool to help instructors better understand and respond appropriately to students' emotions during the online learning process.

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Більше джерел

Дисертації з теми "YOLOv8"

Yesudasu, Santheep. "Cοntributiοn à la manipulatiοn de cοlis sοus cοntraintes par un tοrse humanοïde : applicatiοn à la dépaléttisatiοn autοnοme dans les entrepôts lοgistiques". Electronic Thesis or Diss., Normandie, 2024. https://theses.hal.science/tel-04874770.

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Cette thèse de doctorat explore le développement et l'implémentation d'URNik-AI, un système de dépalettisation automatisé basé sur l'intelligence artificielle (IA), conçu pour manipuler des boîtes en carton de tailles et de poids variés à l'aide d'un torse humanoïde à double bras. L'objectif principal est d'améliorer l'efficacité, la précision et la fiabilité des tâches de dépalettisation industrielle grâce à l'intégration de la robotique avancée, de la vision par ordinateur et des techniques d'apprentissage profond.Le système URNik-AI est composé de deux bras robotiques UR10 équipés de capteurs de force/torque à six axes et d'outils de préhension. Une caméra RGB-D ASUS Xtion est montée sur des servomoteurs pan-tilt Dynamixel Pro H42 pour obtenir des images haute résolution et des données de profondeur. Le cadre logiciel comprend ROS Noetic, ROS 2 et le framework MoveIt, permettant une communication fluide et une coordination des mouvements complexes. Ce système assure une haute précision dans la détection, la saisie et la manipulation d'objets dans divers environnements industriels.Une contribution importante de cette recherche est l'implémentation de modèles d'apprentissage profond, tels que YOLOv3 et YOLOv8, pour améliorer les capacités de détection et d'estimation de pose des objets. YOLOv3, entraîné sur un ensemble de données de 807 images, a atteint des scores F1 de 0,81 et 0,90 pour les boîtes à une et plusieurs faces, respectivement. Le modèle YOLOv8 a encore amélioré les performances du système en fournissant des capacités de détection de points clés et de squelettes, essentielles pour la manipulation précise des objets. L'intégration des données de nuage de points pour l'estimation de la pose a assuré une localisation et une orientation précises des boîtes.Les résultats des tests ont démontré la robustesse du système, avec des métriques élevées de précision, rappel et précision moyenne (mAP), confirmant son efficacité. Cette thèse apporte plusieurs contributions significatives au domaine de la robotique et de l'automatisation, notamment l'intégration réussie des technologies robotiques avancées et de l'IA, le développement de techniques innovantes de détection et d'estimation de pose, ainsi que la conception d'une architecture de système polyvalente et adaptable
This PhD thesis explores the development and implementation of URNik-AI, an AI-powered automated depalletizing system designed to handle cardboard boxes of varying sizes and weights using a dual-arm humanoid torso. The primary objective is to enhance the efficiency, accuracy, and reliability of industrial depalletizing tasks through the integration of advanced robotics, computer vision, and deep learning techniques.The URNik-AI system consists of two UR10 robotic arms equipped with six-axis force/torque sensors and gripper tool sets. An ASUS Xtion RGB-D camera is mounted on Dynamixel Pro H42 pan-tilt servos to capture high-resolution images and depth data. The software framework includes ROS Noetic, ROS 2, and the MoveIt framework, enabling seamless communication and coordination of complex movements. This system ensures high precision in detecting, grasping, and handling objects in diverse industrial environments.A significant contribution of this research is the implementation of deep learning models, such as YOLOv3 and YOLOv8, to enhance object detection and pose estimation capabilities. YOLOv3, trained on a dataset of 807 images, achieved F1-scores of 0.81 and 0.90 for single and multi-face boxes, respectively. The YOLOv8 model further advanced the system's performance by providing keypoint and skeleton detection capabilities, which are essential for accurate grasping and manipulation. The integration of point cloud data for pose estimation ensured precise localization and orientation of boxes.Comprehensive testing demonstrated the system's robustness, with high precision, recall, and mean average precision (mAP) metrics confirming its effectiveness. This thesis makes several significant contributions to the field of robotics and automation, including the successful integration of advanced robotics and AI technologies, the development of innovative object detection and pose estimation techniques, and the design of a versatile and adaptable system architecture

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Oškera, Jan. "Detekce dopravních značek a semaforů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432850.

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The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The training of the model was carried out at a computer center belonging to the virtual organization MetaCentrum VO. Due to the quantifiable evaluation of the detector quality, a program was created statistically and graphically showing its success with use of ROC curve and evaluation protocol COCO. In this thesis I created a model that achieved a success average rate of up to 81 %. The thesis shows the best choice of threshold across versions, sizes and IoU. Extension for mobile phones in TensorFlow Lite and Flutter have also been created.

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Borngrund, Carl. "Machine vision for automation of earth-moving machines : Transfer learning experiments with YOLOv3." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75169.

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This master thesis investigates the possibility to create a machine vision solution for the automation of earth-moving machines. This research was done as without some type of vision system it will not be possible to create a fully autonomous earth moving machine that can safely be used around humans or other machines. Cameras were used as the primary sensors as they are cheap, provide high resolution and is the type of sensor that most closely mimic the human vision system. The purpose of this master thesis was to use existing real time object detectors together with transfer learning and examine if they can successfully be used to extract information in environments such as construction, forestry and mining. The amount of data needed to successfully train a real time object detector was also investigated. Furthermore, the thesis examines if there are specifically difficult situations for the defined object detector, how reliable the object detector is and finally how to use service-oriented architecture principles can be used to create deep learning systems. To investigate the questions formulated above, three data sets were created where different properties were varied. These properties were light conditions, ground material and dump truck orientation. The data sets were created using a toy dump truck together with a similarly sized wheel loader with a camera mounted on the roof of its cab. The first data set contained only indoor images where the dump truck was placed in different orientations but neither the light nor the ground material changed. The second data set contained images were the light source was kept constant, but the dump truck orientation and ground materials changed. The last data set contained images where all property were varied. The real time object detector YOLOv3 was used to examine how a real time object detector would perform depending on which one of the three data sets it was trained using. No matter the data set, it was possible to train a model to perform real time object detection. Using a Nvidia 980 TI the inference time of the model was around 22 ms, which is more than enough to be able to classify videos running at 30 fps. All three data sets converged to a training loss of around 0.10. The data set which contained more varied data, such as the data set where all properties were changed, performed considerably better reaching a validation loss of 0.164 compared to the indoor data set, containing the least varied data, only reached a validation loss of 0.257. The size of the data set was also a factor in the performance, however it was not as important as having varied data. The result also showed that all three data sets could reach a mAP score of around 0.98 using transfer learning.

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Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely. Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object detection model has been trained on two classes of images to investigate the performance impacts of disturbances in the training data set. An expanded data set was created by augmenting the initial data to contain various disturbances. A model was successfully trained on the augmented data set and a correlation between worse performance and presence of noise was detected, but changes in saturation and altered colour levels seemed to have less impact than expected. Reducing noise in gathered data is seemingly of greater importance than enhancing images with lacking colour levels. Further investigations with a larger and more thoroughly processed data set is required to gain a clearer picture of the impact of the various disturbances.

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Núñez-Melgar, Espinoza Erika Pamela, Oré Natali Leonor Reyes, Abad Jorge Raúl Salazar, and Vela Anderson Vásquez. "YOLO." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/625370.

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El siguiente trabajo de investigación pretende comprobar la viabilidad del proyecto denominado YOLO. Este proyecto propone crear un medio virtual para interrelacionar dos segmentos de intereses o necesidades diferentes: un segmento que desea vender productos y otro segmento que desea obtenerlos participando en un proceso de rifa virtual a precios accesibles. En la encuesta virtual realizada para conocer el interés del servicio en el mercado, los resultados que se obtuvieron fueron que, un 72% estaría dispuesto a participar en juegos de azar virtuales y que un 52% ha vendido algún producto nuevo o usado por internet. Para este proyecto se requiere una inversión aproximada de 141,000.00 nuevos soles de los cuales el 60% corresponde a capital de los accionistas y el 40% restante será financiado por una entidad financiera. El proyecto YOLO que presentamos contiene el plan estratégico, el plan de marketing, el plan de operaciones, recursos humanos, y el plan económico-financiero. En la evaluación de los flujos de caja que genera el proyecto nos indican que el 100% de la inversión inicial se recupera en el primer año. Asimismo, los accionistas, desde el primer año ya tienen libre disposición de efectivo, el cual crece 23% cada año. Por último, el VAN, para ambos flujos, son positivos y la TIR, para ambos flujos, generan tasas mayores al COK de los accionistas. Concluimos que le proyecto de negocio genera valor a los accionistas, es viable y rentable.
The following research work pretends to test out the viability of the project named YOLO. This project proposes to create a virtual setting to interrelate two segments of different interests or needs: a segment that wants to sell products such as technology, clothing or accessories and another segment that wish to get those items participating in a virtual raffle at affordable prices. In a virtual survey conducted to know the interest and value of the service in the market, the results were that 72% of the people would be willing to participate in virtual games of chance and 52% has sold some new or used product by the Internet. Afterward, the proposal was validated through the raffle of a product in which the interest and participation of the users were astounding because it surpassed the expectations. For this project, it is required an investment of approximately S/141,000.00 of which the 60% corresponds to the shareholders capital and the remaining 40% will be financed by a financial institution. Finally, we present for your revision, the assessment process of project YOLO, it contains the strategic plan, the marketing plan, the operations plan, human resources, and the economic-financial plan. For that reason, in this summary more reference has been made to the financial aspect basing us in the evaluation of cash flows that create the project and which indicate that 100% of the initial outlay it is regained during the first year, all the same, happens to the shareholders who have already free disposition of cash on the first year, which grows 23% every year. Additionally, Net Present Value (NPV), for both flows, is positive and the Internal Rate of Return (IRR), for both flows, generates higher rates than the shareholder's opportunity cost of capital It concludes that the business project generates value to the stockholders, it is viable and profitable.
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Norling, Samuel. "Tree species classification with YOLOv3 : Classification of Silver Birch (Betula pendula) and Scots Pine (Pinus sylvestris)." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260244.

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Automation of tree species classification during a forest inventory could potentially provide more efficiency and better results for forest companies and stakeholding agencies. This thesis investigates how well a state of the art object detection system, YOLOv3, performs this classification task. A new image dataset with pictures of Silver Birches and Scots Pines, called LilljanNet, was created to train YOLOv3. After training YOLOv3 on half the dataset we performed validation by testing it against the other half. The trained model scored a mean average precision above 0.99. Training was also done with smaller sets of training data and the mean average precision score for these models all achieved mean average precision above 0.95. The results are promising and further research should be done testing this on smartphones and drones.
Automatisering av trädslagsklassifiering vid en skogstaxering skulle potentiellt sätt kunna ge mer effektivitet och bättre resultat för skogsbolag och myndigheter som ansvarar för skogen. Denna uppsats undersöker hur väl ett toppmodernt datorseendesystem, YOLOv3, utför denna klassifieringsuppgift. Ett nytt bildbibliotek med bilder av björkar och tallar, som kallas LilljanNet, skapades för att träna YOLOv3. Efter vi tränat YOLOv3 på halva datamängden utförde vi validering mot den andra halvan. Den upptränade modellen uppnådde ett mean average precision över 0.99. Träning gjordes också med mindre mängder träningsdata och mean average precision-resultaten för dessa modeller var alltid över 0.95. Resultaten är lovande och mer forskning bör göras där man testar att implementera detta på smartphones och drönare.

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Ståhl, Sebastian. "A tracking framework for a dynamic non- stationary environment." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288955.

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As the use of unmanned aerial vehicles (UAVs) increases in popularity across the globe, their fields of application are constantly growing. This thesis researches the possibility of using a UAV to detect, track, and geolocate a target in a dynamic nonstationary environment as the seas. In this case, different projection and apparent size of the target in the captured images can lead to ambiguous assignments of coordinated. In this thesis, a framework based on a UAV, a monocular camera, a GPS receiver, and the UAV’s inertial measurement unit (IMU) is developed to perform the task of detecting, tracking and geolocating targets. An object detection model called Yolov3 was retrained to be able to detect boats in UAV footage. This model was selected due to its capabilities of detecting targets of small apparent sizes and its performance in terms of speed. A model called the kernelized correlation filter (KCF) is adopted as the visual tracking algorithm. This tracker is selected because of its performance in terms of speed and accuracy. A reinitialization of the tracker in combination with a periodic update of the tracked bounding box are implemented which resulted in improved performance of the tracker. A geolocation method is developed to continuously estimate the GPS coordinates of the target. These estimates will be used by the flight control method already developed by the stakeholder Airpelago to control the UAV. The experimental results show promising results for all models. Due to inaccurate data, the true accuracy of the geolocation method can not be determined. The average error calculated with the inaccurate data is 19.5 meters. However, an in- depth analysis of the results indicates that the true accuracy of the method is more accurate. Hence, it is assumed that the model can estimate the GPS coordinates of a target with an error significantly lower than 19.5 meters. Thus, it is concluded that it is possible to detect, track and geolocate a target in a dynamic nonstationary environment as the seas.
Användandet av drönare ökar i popularitet över hela världen vilket bidrar till att dess tillämpningsområden växer. I denna avhandling undersöks möjligheten att använda en drönare för att detektera, spåra och lokalisera ett mål i en dynamisk icke- stationär miljö som havet. Målets varierande position och storlek i bilderna leda till tvetydiga uppgifter. I denna avhandlingen utvecklas ett ramverk baserat på en drönare, en monokulär kamera, en GPS- mottagare och drönares IMU sensor för att utföra detektering, spårning samt lokalisering av målet. En objektdetekteringsmodell vid namn Yolov3 tränades för att kunna detektera båtar i bilder tagna från en drönare. Denna modell valdes på grund av dess förmåga att upptäcka små mål och dess prestanda vad gäller hastighet. En modell vars förkortning är KCF används som den visuella spårningsalgoritmen. Denna algoritm valdes på grund av dess prestanda när det gäller hastighet och precision. En återinitialisering av spårningsalgoritmen i kombination med en periodisk uppdatering av den spårade avgränsningsrutan implementeras vilket förbättrar trackerens prestanda. En lokaliseringsmetod utvecklas för att kontinuerligt uppskatta GPS- koordinaterna av målet. Dessa uppskattningar kommer att användas av en flygkontrollmetod som redan utvecklats av Airpelago för att styra drönaren. De experimentella resultaten visar lovande resultat för alla modeller. På grund av opålitlig data kan inte lokaliseringsmetodens precision fastställas med säkerhet. En djupgående analys av resultaten indikerar emellertid att metodens noggrannhet är mer exakt än det genomsnittliga felet beräknat med opålitliga data, som är 19.5 meter. Därför antas det att modellen kan uppskatta GPS- koordinaterna för ett mål med ett fel som är lägre än 19.5 meter. Således dras slutsatsen att det är möjligt att upptäcka, spåra och geolocera ett mål i en dynamisk icke- stationär miljö som havet.

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Ye, Fanjie. "A Method of Combining GANs to Improve the Accuracy of Object Detection on Autonomous Vehicles." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752364/.

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As the technology in the field of computer vision becomes more and more mature, the autonomous vehicles have achieved rapid developments in recent years. However, the object detection and classification tasks of autonomous vehicles which are based on cameras may face problems when the vehicle is driving at a relatively high speed. One is that the camera will collect blurred photos when driving at high speed which may affect the accuracy of deep neural networks. The other is that small objects far away from the vehicle are difficult to be recognized by networks. In this paper, we present a method to combine two kinds of GANs to solve these problems. We choose DeblurGAN as the base model to remove blur in images. SRGAN is another GAN we choose for solving small object detection problems. Due to the total time of these two are too long, we still do the model compression on it to make it lighter. Then we use the Yolov4 to do the object detection. Finally we do the evaluation of the whole model architecture and proposed a model version 2 based on DeblurGAN and ESPCN which is faster than previous one but the accuracy may be lower.

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Wang, Chen. "2D object detection and semantic segmentation in the Carla simulator." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291337.

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The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decision-making of autonomous vehicles before implementation in real-world cars. This project aims to build a benchmark for implementing the whole self-driving car system in software. There are three main components including perception, planning, and control in the entire autonomous driving system. This thesis focuses on two sub-tasks 2D object detection and semantic segmentation in the perception part. All of the experiments will be tested in a simulator environment called The CAR Learning to Act(Carla), which is an open-source platform for autonomous car research. Carla simulator is developed based on the game engine(Unreal4). It has a server-client system, which provides a flexible python API. 2D object detection uses the You only look once(Yolov4) algorithm that contains the tricks of the latest deep learning techniques from the aspect of network structure and data augmentation to strengthen the network’s ability to learn the object. Yolov4 achieves higher accuracy and short inference time when comparing with the other popular object detection algorithms. Semantic segmentation uses Efficient networks for Computer Vision(ESPnetv2). It is a light-weight and power-efficient network, which achieves the same performance as other semantic segmentation algorithms by using fewer network parameters and FLOPS. In this project, Yolov4 and ESPnetv2 are implemented into the Carla simulator. Two modules work together to help the autonomous car understand the world. The minimal distance awareness application is implemented into the Carla simulator to detect the distance to the ahead vehicles. This application can be used as a basic function to avoid the collision. Experiments are tested by using a single Nvidia GPU(RTX2060) in Ubuntu 18.0 system.
Ämnet självkörande bilteknik har väckt intresse de senaste åren. Många företag, som Baidu och Tesla, har redan infört automatiska körtekniker i sina nyaste bilar när de kör i ett specifikt område. Det finns dock fortfarande många utmaningar inför fullt autonoma bilar. Detta projekt syftar till att bygga ett riktmärke för att implementera hela det självkörande bilsystemet i programvara. Det finns tre huvudkomponenter inklusive uppfattning, planering och kontroll i hela det autonoma körsystemet. Denna avhandling fokuserar på två underuppgifter 2D-objekt detektering och semantisk segmentering i uppfattningsdelen. Alla experiment kommer att testas i en simulatormiljö som heter The CAR Learning to Act (Carla), som är en öppen källkodsplattform för autonom bilforskning. Du ser bara en gång (Yolov4) och effektiva nätverk för datorvision (ESPnetv2) implementeras i detta projekt för att uppnå Funktioner för objektdetektering och semantisk segmentering. Den minimala distans medvetenhets applikationen implementeras i Carla-simulatorn för att upptäcka avståndet till de främre bilarna. Denna applikation kan användas som en grundläggande funktion för att undvika kollisionen.

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Ferrer, Bustamante Claudia Mariela, Llanos Víctor Hugo Ibarra, and Flores Carlos Rafael Prialé. "Plataforma virtual de Rifa Yolo." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/625450.

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El presente proyecto de negocio ha sido trabajado con la finalidad de atender la necesidad de las personas que son usuarias del comercio electrónico ofreciéndoles una manera innovadora de obtener productos por un costo mínimo. El objetivo de este plan es aproximar productos a los consumidores que tienen el deseo de tenerlos pero que por diversas razones no han podido conseguirlos. En esta propuesta elaborada para cumplir el deseo de nuestro cliente elegido, se ha trabajado en la identificación de sus principales motivaciones al momento de comprar por internet como son: el ahorro de tiempo, la conveniencia y la búsqueda del mejor precio, lo cual se contrastó con los principales problemas que enfrentan al momento de hacer compras en un espacio físico: prolongada espera para ser atendido y pagar, precios altos de los productos y mala atención. Además, se logró evidenciar sus temores respecto a la experiencia de comprar por internet. El proyecto de negocio plantea el servicio de sortear productos de marcas reconocidas por nuestro cliente por lo cual se pondrá a la venta boletos de rifas durante un tiempo establecido para cada sorteo, esto se realizará a través de una plataforma virtual ágil y confiable donde el cliente podrá comprar cuantas opciones desee para ganar. Nuestra propuesta de valor propone el envío del producto libre de costo, transmisiones en vivo y notificaciones de todos los sorteos que serán debidamente validados, variedad de productos, garantía, medios de pago sencillos, bonificaciones.
This business project has been developed with the purpose of meeting the needs of people who are users of electronic commerce by offering an innovative way to obtain products for a minimal cost. The objective of this plan is to bring products closer to consumers who have the desire to have them but for various reasons have not been able to get them. In this proposal developed to fulfill the desire of our chosen client, we have worked on identifying their main motivations when buying online such as: saving time, convenience and searching for the best price, which was contrasted with the main problems they face when making purchases in a physical space: prolonged waiting to be attended and paid, high prices of products and poor attention. In addition, it was possible to highlight their fears regarding the experience of shopping online. The business project raises the service of raffling products of brands recognized by our client, for which raffle tickets will be sold for a set time for each draw, this will be done through an agile and reliable virtual platform where the customer can buy as many options as he wants in order to win the raffle. Our value proposition proposes sending the product free of cost, live broadcasts and notifications of all the draws that will be duly validated, variety of products, guarantee, simple means of payment, bonuses.
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Книги з теми "YOLOv8"

Jones, Sam. Yolo. New York: Simon Pulse, 2014.

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1564-1616, Shakespeare William, ed. YOLO Juliet. New York: Random House, 2015.

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Oxlajuuj Keej Maya' Ajtz'iib' (Group). and Centro Educativo y Cultural Maya., eds. Jkemiik yoloj li uspanteko =: Gramática uspanteka. Antigua, Guatemala: OKMA, 2007.

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O, Obemeata Joseph, Ayodele Samuel O, Araromi M. A, and Yoloye E. Ayotunde, eds. Evaluation in Africa: In honour of Professor E.A. Yoloye. Ibadan, Nigeria: Stirling-Horden Publishers, 1999.

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Academia de las Lenguas Mayas de Guatemala, ed. Yolooj chib' jb'iijaq aj Tz'unun Kaab' =: Nombres y apellidos uspantekos. Uspantán, [Guatemala]: Academia de Lenguas Mayas de Guatemala, 2003.

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Uspanteka, Comunidad Lingüística, ed. Yolooj chib' jb'iijaq aj Tz'unun Kaab': Nombres y apellidos uspantekos. Uspantán, El Quiché [Guatemala]: Academia de Lenguas Mayas de Guatemala, 2003.

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Stevens, James L., and Rosenberg David. Judges of Yolo County: 1850-1985. [United States]: [s.n.], 2011.

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Kärimova, Häqiqät. Şäräfli ömür yolo: Vagif Abbasov-50. Bakı: Tähsil, 2002.

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California. Dept. of Water Resources. Central District., ed. Historical ground water levels in Yolo County. Sacramento, CA (P.O. Box 942836, Sacramento 94236-0001): Dept. of Water Resources, Central District, 1992.

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Zentner & Zentner. Cache Creek environmental restoration program, Yolo County, California. Walnut Creek, Calif: Zentner & Zentner, 1993.

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Частини книг з теми "YOLOv8"

Thanh, Bui Dang, Mac Tuan Anh, Giap Dang Khanh, Trinh Cong Dong, and Nguyen Thanh Huong. "SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8." In Communications in Computer and Information Science, 170–80. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70906-7_15.

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Taskin, Elif Melis. "Interactive Neural Network for Object Detection in YOLOv5 and YOLOv8." In Information Systems Engineering and Management, 382–92. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69197-3_30.

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Alves, Adília, José Pereira, Salik Khanal, A. Jorge Morais, and Vitor Filipe. "Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models." In Communications in Computer and Information Science, 50–62. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53036-4_4.

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Kapil, Bhavesh, and Kamlesh Dutta. "Fabric Defects Detection Using YOLOv8." In Lecture Notes in Networks and Systems, 405–19. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6992-6_30.

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Xing, Zhecong, Yuan Zhu, Rui Liu, Weiqi Wang, and Zhiguo Zhang. "DCM-YOLOv8: An Improved YOLOv8-Based Small Target Detection Model for UAV Images." In Lecture Notes in Computer Science, 367–79. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5597-4_31.

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Abbas, Shahad Fadhil, Shaimaa Hameed Shaker, and Firas A. Abdullatif. "YOLOv8-AS: Masked Face Detection and Tracking Based on YOLOv8 with Attention Mechanism Model." In Communications in Computer and Information Science, 267–75. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62814-6_19.

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Bharadwaja, D., G. Bhavya Sri, Abdul Azeez, and K. Nikitha. "Real Time Surveillance System Using Yolov8." In Information Systems Engineering and Management, 109–18. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69197-3_9.

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Salekin, Siraj Us, Md Hasib Ullah, Abdullah Al Ahad Khan, Md Shah Jalal, Huu-Hoa Nguyen, and Dewan Md Farid. "Bangladeshi Native Vehicle Classification Employing YOLOv8." In Communications in Computer and Information Science, 185–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7649-2_14.

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Prakash, Immidisetty V., and M. Palanivelan. "A Study of YOLO (You Only Look Once) to YOLOv8." In Algorithms in Advanced Artificial Intelligence, 257–66. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003529231-40.

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Selcuk, Burcu, and Tacha Serif. "A Comparison of YOLOv5 and YOLOv8 in the Context of Mobile UI Detection." In Mobile Web and Intelligent Information Systems, 161–74. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39764-6_11.

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Тези доповідей конференцій з теми "YOLOv8"

Ikmel, Ghita, and EL AMRANI EL IDRISSI Najiba. "Performance Analysis of YOLOv5, YOLOv7, YOLOv8, and YOLOv9 on Road Environment Object Detection: Comparative Study." In 2024 International Conference on Ubiquitous Networking (UNet), 1–5. IEEE, 2024. https://doi.org/10.1109/unet62310.2024.10794724.

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Mohajeran, Seena, Hannah Ke, Jenna Ke, Michelle Li, Yu Bai, and Macy Li. "Streamlined Video Object Detection with YOLOX YOLOV5 YOLOV7 and YOLOV8." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 664–69. IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708395.

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A’la, Fiddin Yusfida, Muhammad Asri Safi'ie, and Andy Supriyadi. "YOLOv8 vs. YOLOv9: Safety Helmet Detection Performance." In 2024 7th International Conference of Computer and Informatics Engineering (IC2IE), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/ic2ie63342.2024.10748076.

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Chomklin, Amonpan, Saichon Jaiyen, Niwan Wattanakitrungroj, Pornchai Mongkolnam, and Suluk Chaikhan. "Packaging Defect Detection in Lean Manufacturing: A Comparative Study of YOLOv8, YOLOv9, and YOLOv10." In 2024 28th International Computer Science and Engineering Conference (ICSEC), 1–6. IEEE, 2024. https://doi.org/10.1109/icsec62781.2024.10770712.

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Liang, YuYing, and Xin Chen. "YOLOv8-AMCD: Improved YOLOv8 for Small Object Detection." In 2024 6th International Conference on Robotics and Computer Vision (ICRCV), 33–37. IEEE, 2024. http://dx.doi.org/10.1109/icrcv62709.2024.10758598.

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Li, Xiaolin, and Lishun Ma. "DED-YOLOv8:Dense pedestrian detection algorithm based on YOLOv8." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS), 545–48. IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10699141.

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Liu, Ruiyu, Hanlin Zhang, Zhe Liu, and Dan Chen. "CAE-YOLOV8: Occlusion Object Detection Based on Improved YOLOv8." In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), 480–83. IEEE, 2024. http://dx.doi.org/10.1109/icmlca63499.2024.10753964.

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Liu, Shenghu, and Musha Yasenjiang. "GGM-YOLOV8: Strawberry Disease Detection Model Based on Improved YOLOv8." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), 1153–57. IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653090.

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Yang, Guoyuan, Wei Xiong, Jiaqi Lei, and Kang Yang. "Blade-YOLOv8:Improved YOLOv8 for Wind Turbine Blade Defect Detection." In 2024 4th Power System and Green Energy Conference (PSGEC), 209–13. IEEE, 2024. http://dx.doi.org/10.1109/psgec62376.2024.10721051.

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Kich, Victor A., Muhammad A. Muttaqien, Junya Toyama, Ryutaro Miyoshi, Yosuke Ida, Akihisa Ohya, and Hisashi Date. "Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments." In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM), 514–19. IEEE, 2024. http://dx.doi.org/10.1109/cis-ram61939.2024.10673292.

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Звіти організацій з теми "YOLOv8"

Schoening, Timm. PyiFDOYOLO. GEOMAR, December 2022. http://dx.doi.org/10.3289/sw_2_2022.

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Yoo, Shinjae, Yonggang Cui, Ji Hwan Park, Yuewei Lin, and Yihui Ren. Development of a software tool for IAEA use of the YOLOv3 machine learning algorithm. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1494041.

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Cui, Yonggang, S. Yoo, and J. Hwan Park. YOLO Test Software v1.2. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1646872.

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Mohamed, Amna. Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models. Ames (Iowa): Iowa State University, May 2023. http://dx.doi.org/10.31274/cc-20240624-1513.

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Speer, B. First Known Use of QECBs will Save Yolo County at Least $8.7 Million Over the Next 25 Years, Energy Analysis (Revised) (Brochure). Office of Scientific and Technical Information (OSTI), June 2011. http://dx.doi.org/10.2172/1008195.

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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, July 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Анотація:

Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, and user experience. The program utilizes two machine learning models. The first model, Visual Geometry Group (VGG16), uses deep learning neural network technology to classify potholes with over 90% accuracy. The second machine learning model, You Only Look Once (YOLO), has been designed to detect and accurately mark potholes on submitted photos. Overall, this innovative pothole management program offers a comprehensive solution to help address the critical issue of potholes in urban areas.

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Forero Fuarez, Luis Carlos. Procesamiento de imágenes. Escuela Tecnológica Instituto Técnico Central - ETITC, 2023. http://dx.doi.org/10.55411/2023.4.

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El semillero tiene como uno de sus objetivos, la enseñanza y la aplicación de técnicas y herramientas de inteligencia artificial en áreas de la ingeniería electromecánica y afines. Para ello se seguirá un proceso que requerirá en sus primeras etapas la recopilación de la información, su limpieza, transformación y análisis, persiguiendo mediante el aprendizaje continuo de los estudiantes y su desarrollo en posteriores etapas, la implementación de modelos y/o arquitecturas que permitan desarrollar un modelo de IA basado en técnicas de visión por computadora y aprendizaje automático para reconocer las placas de los vehículos que ingresan a la ETITC en tiempo real y/o aplicaciones en general, como procesos de regresión, clasificación, segmentación, etc. Considerando que a futuro se planteará el trabajar con imágenes, se sabe que este campo presenta gran auge en distintos campos, pues como lo menciona LeCun et al. 2015, el uso de redes convolucionales ha ampliado la capacidad para extraer características relevantes de las imágenes, lo que es fundamental para el reconocimiento de placas de vehículos. Adicionalmente, se han desarrollado métodos como el introducido por Redmon J et al. (2016), el cual es conocido actualmente como YOLO "You Only Look Once" que mediante redes convolucionales facilita el reconocimiento de objetos. Adicionalmente se tiene el ejemplo de Krizhevsky, A (2012), quien mediante el modelo AlexNet, presentó gran eficacia en tareas de reconocimiento de imágenes.

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Chemical quality of ground water in Yolo and Solano counties, California. US Geological Survey, 1985. http://dx.doi.org/10.3133/wri844244.

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Streamflow, sediment discharge, and streambank erosion in Cache Creek, Yolo County, California, 1953-86. US Geological Survey, 1989. http://dx.doi.org/10.3133/wri884188.

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Hydrology and chemistry of floodwaters in the Yolo Bypass, Sacramento River system, California, during 2000. US Geological Survey, 2002. http://dx.doi.org/10.3133/wri024202.

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