Relevant bibliographies by topics / YOLOX

Academic literature on the topic 'YOLOX'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'YOLOX.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "YOLOX":

1

Mao, Yitong. "A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm." MATEC Web of Conferences 355 (2022): 03020. http://dx.doi.org/10.1051/matecconf/202235503020.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.
2

Deng, Xiangwu, Long Qi, Zhuwen Liu, Song Liang, Kunsong Gong, and Guangjun Qiu. "Weed target detection at seedling stage in paddy fields based on YOLOX." PLOS ONE 18, no. 12 (December 13, 2023): e0294709. http://dx.doi.org/10.1371/journal.pone.0294709.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Weeds are one of the greatest threats to the growth of rice, and the loss of crops is greater in the early stage of rice growth. Traditional large-area spraying cannot selectively spray weeds and can easily cause herbicide waste and environmental pollution. To realize the transformation from large-area spraying to precision spraying in rice fields, it is necessary to quickly and efficiently detect the distribution of weeds. Benefiting from the rapid development of vision technology and deep learning, this study applies a computer vision method based on deep-learning-driven rice field weed target detection. To address the need to identify small dense targets at the rice seedling stage in paddy fields, this study propose a method for weed target detection based on YOLOX, which is composed of a CSPDarknet backbone network, a feature pyramid network (FPN) enhanced feature extraction network and a YOLO Head detector. The CSPDarknet backbone network extracts feature layers with dimensions of 80 pixels ⊆ 80 pixels, 40 pixels ⊆ 40 pixels and 20 pixels ⊆ 20 pixels. The FPN fuses the features from these three scales, and YOLO Head realizes the regression of the object classification and prediction boxes. In performance comparisons of different models, including YOLOv3, YOLOv4-tiny, YOLOv5-s, SSD and several models of the YOLOX series, namely, YOLOX-s, YOLOX-m, YOLOX-nano, and YOLOX-tiny, the results show that the YOLOX-tiny model performs best. The mAP, F1, and recall values from the YOLOX-tiny model are 0.980, 0.95, and 0.983, respectively. Meanwhile, the intermediate variable memory generated during the model calculation of YOLOX-tiny is only 259.62 MB, making it suitable for deployment in intelligent agricultural devices. However, although the YOLOX-tiny model is the best on the dataset in this paper, this is not true in general. The experimental results suggest that the method proposed in this paper can improve the model performance for the small target detection of sheltered weeds and dense weeds at the rice seedling stage in paddy fields. A weed target detection model suitable for embedded computing platforms is obtained by comparing different single-stage target detection models, thereby laying a foundation for the realization of unmanned targeted herbicide spraying performed by agricultural robots.
3

Li, Quanyang, Zhongqiang Luo, Xiangjie He, and Hongbo Chen. "LA_YOLOx: Effective Model to Detect the Surface Defects of Insulative Baffles." Electronics 12, no. 9 (April 27, 2023): 2035. http://dx.doi.org/10.3390/electronics12092035.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
In the field of industry, defect detection based on YOLO models is widely used. In real detection, the method of defect detection of insulative baffles is artificial detection. The work efficiency of this method, however, is low because the detection is depends absolutely on human eyes. Considering the excellent performance of YOLOx, an intelligent detection method based on YOLOx is proposed. First, we selected a CIOU loss function instead of an IOU loss function by analyzing the defect characteristics of insulative baffles. In addition, considering the limitation of model resources in application scenarios, the lightweight YOLOx model is proposed. We replaced YOLOx’s backbone with lightweight backbones (MobileNetV3 and GhostNet), and used Depthwise separable convolution instead of conventional convolution. This operation reduces the number of network parameters by about 42% compared with the original YOLOx network. However, the mAP of it is decreased by about 0.8% compared with the original YOLOx model. Finally, the attention mechanism is introduced into the feature fusion module to solve this problem, and we called the lightweight YOLOx with an attention module LA_YOLOx. The final value of mAP of LA_YOLOx reaches 95.60%, while the original YOLOx model is 95.31%, which proves the effectiveness of the LA_YOLOx model.
4

Zhang, Yao, Ke Jiong Shen, Zhen Fang He, and Zhi Song Pan. "YOLO-infrared: Enhancing YOLOX for Infrared Scene." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2405/1/012015.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Abstract Draw a bead on the specialty of infrared scenes and tackling the disequilibrium between positive and negative samples in object detectors, this paper introduces an object detection model for infrared scenes named YOLO-infrared based on YOLOX. This paper first analyses the shortcomings of the object detection model designed for the visible domain when applied to the infrared domain by visualizing the feature heat map of the YOLOX neck network. Considering the blurred edges of the target in the infrared image, which almost blends with the background in terms of colour and texture, with little distinction between pixel points at close distances, this paper first employs an attention module to extract the position relationship between distant pixels, which enhances the feature abstracting capacity of YOLOX. Finally, DR Loss is utilized to address the issue of positive and negative sample imbalance in the object detection process. The model YOLO-infrared proposed in this paper achieves 38.2% and 38.6% on the FLIR and KAIST datasets, respectively, which is at least 0.5% higher than the current SOAT detector.
5

Wang, Yaxin, Xinyuan Liu, Fanzhen Wang, Dongyue Ren, Yang Li, Zhimin Mu, Shide Li, and Yongcheng Jiang. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection." Sustainability 15, no. 19 (October 3, 2023): 14437. http://dx.doi.org/10.3390/su151914437.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Fuel types are essential for the control systems of briquette biofuel boilers, as the optimal combustion condition varies with fuel type. Moreover, the use of coal in biomass boilers is illegal in China, and the detection of coals will, in time, provide effective information for environmental supervision. This study established a briquette biofuel identification method based on the object detection of fuel images, including straw pellets, straw blocks, wood pellets, wood blocks, and coal. The YoloX-S model was used as the baseline network, and the proposed model in this study improved the detection performance by adding the self-attention mechanism module. The improved YoloX-S model showed better accuracy than the Yolo-L, YoloX-S, Yolov5, Yolov7, and Yolov8 models. The experimental results regarding fuel identification show that the improved model can effectively distinguish biomass fuel from coal and overcome false and missed detections found in the recognition of straw pellets and wood pellets by the original YoloX model. However, the interference of the complex background can greatly reduce the confidence of the object detection method using the improved YoloX-S model.
6

Ashraf, Imran, Soojung Hur, Gunzung Kim, and Yongwan Park. "Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions." Sensors 24, no. 2 (January 14, 2024): 522. http://dx.doi.org/10.3390/s24020522.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.
7

Raimundo, António, João Pedro Pavia, Pedro Sebastião, and Octavian Postolache. "YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections." Sensors 23, no. 10 (May 11, 2023): 4681. http://dx.doi.org/10.3390/s23104681.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.
8

Слюсар, Вадим Іванович Слюсар. "Нейромережний метод підводного виявлення боєприпасів, що не спрацювали." Известия высших учебных заведений. Радиоэлектроника 65, no. 12 (December 26, 2022): 766–77. http://dx.doi.org/10.20535/s0021347023030020.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
В статье обоснованы предложения по применению нейронных сетей семейства YOLO для обнаружения не сработавших подводных боеприпасов. При этом были использованы предварительно обученные на датасете MS COCO нейронные сети YOLO3, YOLO4 и YOLO5. Дообучение нейросетей YOLO3 и YOLO 4 осуществлялось на модифицированном датасете подводного мусора Trash-ICRA19, количество классов объектов в котором составляло 13, из которых 2 были фиктивными. При этом среднеклассовая точность детектирования 13 классов объектов с помощью YOLO4 по метрике mAP50 составляла 75.2% или, с учетом фиктивных классов, 88.873%. Для тестирования нейросетей были использованы изображения, полученные из видеозаписей процесса разминирования водоемов с помощью дистанционно управляемых подводных аппаратов (ROV). Предложена усовершенствованная схема нейронной сети, представляющая собой каскад из нескольких последовательно соединенных YOLO-сегментов с многопроходной обработкой изображений. Разработаны рекомендации по дальнейшему повышению эффективности нейросетевого метода селекции подводных боеприпасов.
9

Zhou, Xinzhu, Guoxiang Sun, Naimin Xu, Xiaolei Zhang, Jiaqi Cai, Yunpeng Yuan, and Yinfeng Huang. "A Method of Modern Standardized Apple Orchard Flowering Monitoring Based on S-YOLO." Agriculture 13, no. 2 (February 4, 2023): 380. http://dx.doi.org/10.3390/agriculture13020380.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Monitoring fruit tree flowering information in the open world is more crucial than in the research-oriented environment for managing agricultural production to increase yield and quality. This work presents a transformer-based flowering period monitoring approach in an open world in order to better monitor the whole blooming time of modern standardized orchards utilizing IoT technologies. This study takes images of flowering apple trees captured at a distance in the open world as the research object, extends the dataset by introducing the Slicing Aided Hyper Inference (SAHI) algorithm, and establishes an S-YOLO apple flower detection model by substituting the YOLOX backbone network with Swin Transformer-tiny. The experimental results show that S-YOLO outperformed YOLOX-s in the detection accuracy of the four blooming states by 7.94%, 8.05%, 3.49%, and 6.96%. It also outperformed YOLOX-s by 10.00%, 9.10%, 13.10%, and 7.20% for mAPALL, mAPS, mAPM, and mAPL, respectively. By increasing the width and depth of the network model, the accuracy of the larger S-YOLO was 88.18%, 88.95%, 89.50%, and 91.95% for each flowering state and 39.00%, 32.10%, 50.60%, and 64.30% for each type of mAP, respectively. The results show that the transformer-based method of monitoring the apple flower growth stage utilized S-YOLO to achieve the apple flower count, percentage analysis, peak flowering time determination, and flowering intensity quantification. The method can be applied to remotely monitor flowering information and estimate flowering intensity in modern standard orchards based on IoT technology, which is important for developing fruit digital production management technology and equipment and guiding orchard production management.
10

Pan, Haixia, Jiahua Lan, Hongqiang Wang, Yanan Li, Meng Zhang, Mojie Ma, Dongdong Zhang, and Xiaoran Zhao. "UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection." Sensors 23, no. 10 (May 18, 2023): 4859. http://dx.doi.org/10.3390/s23104859.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.
More sources
You might also be interested in the extended bibliographies on the topic 'YOLOX' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "YOLOX":

1

Nikue, Amassah Djahlin. "Analyse vidéo pour la détection, le suivi et la reconnaissance du comportement pour l'animal en situation d'élevage." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
La reconnaissance d'activités, également connue sous le nom de reconnaissance d'actions, est un domaine de recherche en vision par ordinateur et en apprentissage automatique, avec diverses applications. L'application la plus courante est l'identification et la compréhension des activités humaines à partir de données visuelles, telles que des images ou des vidéos. Les techniques de reconnaissance d'actions peuvent être appliquées également à la surveillance du bétail, où elles contribuent à améliorer le bien-être des animaux, la productivité et les pratiques de gestion agricole. Ainsi, les travaux réalisés dans ce document se situent dans le cadre de l'analyse vidéo pour la détection, le suivi et la reconnaissance du comportement animal en situation d'élevage. Ces travaux sont réalisés au sein de ANIMOV « Animal Movements Observation from Videos », un projet de recherche pluridisciplinaire mis en œuvre sur la période 2019-2023 par un consortium régional en Centre-Val-de-Loire. Ce projet porte principalement sur deux espèces animales : les éléphants et les chèvres. Dans ce mémoire, nos recherches portent sur l'analyse des activités chez les chèvres. Afin de construire notre système d'analyse du comportement, nous avons mis en place un système de détection et de suivi d'objets. Pour la détection nous avons testé et comparé deux méthodes populaires de la littérature : YOLOv4 et Faster R-CNN, sur des bases de données créées par nos soins. Parmi les deux méthodes de détection, YOLOv4 présente de meilleures performances en terme de précision moyenne et est 2.5 fois plus rapide que le Faster R-CNN. Pour le suivi des chèvres, nous avons testé et comparé également deux méthodes populaires de la littérature : SORT et Deep SORT. L'évaluation des deux méthodes de suivi sur les vidéos de test montre une légère amélioration de Deep SORT par rapport à SORT en terme d'association des données. Cependant, SORT reste plus rapide et plus adapté à un système temps réel. Le système de détection et de suivi mis en place, nous permet de réaliser, en temps réel, l'analyse de l'activité générale du troupeau, avec des indicateurs assez proches de la réalité. La principale faiblesse dans notre système est la perte de détection sur certaines images de la vidéo, qui entraîne des échecs dans le suivi. Ainsi, pour améliorer les performances, nous avons proposé une approche qui fusionne les informations des détections précédentes et de l'image courante, dans une nouvelle architecture de détection (YOLOX), afin de mieux détecter tous les objets sans perdre les anciens
Activity recognition, also known as action recognition, is a field of research in computer vision and machine learning, with a variety of applications. One of the most common applications is the identification and understanding of human activities from visual data, such as images or videos. Action recognition techniques can also be applied to livestock monitoring, where they can help improve animal welfare, productivity, and farm management practices. Thus, the work conducted in this document falls within the context of video analysis for the detection, monitoring, and recognition of animal behavior in livestock situations. This work is being achieved within ANIMOV "Animal Movements Observation from Videos", a multidisciplinary research project being implemented over the period 2019-2023 by a regional consortium in Centre-Val-de-Loire. This project concerns two main animal species: elephants and goats. In this thesis, our research focuses on activity analysis for goats. We have built an object detection and tracking system to implement our behavior analysis system. For detection, we tested and compared two popular methods from the literature: YOLOv4 and Faster R-CNN, on self-created datasets. Of the two detection methods, YOLOv4 performs better in average accuracy and is 2.5 times faster than Faster R-CNN. For goat tracking, we also tested and compared two popular methods from the literature: SORT and Deep SORT. Evaluation of both tracking methods on test videos shows a slight improvement of Deep SORT over SORT regarding data association. However, SORT is faster and better suited to a real-time system. The detection and tracking system we have set up enables us to analyze the general activity of the livestock in real-time, with indicators that are fairly close to reality. The main limitation of our system is the loss of detection on certain video images, which leads to tracking failures. So, to improve the performance, we proposed an approach that merges information from previous detections and the current image, in a new detection architecture (YOLOX), to better detect all objects without losing the old ones
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
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.
Trabajo de investigación
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
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.
Trabajo de investigación
4

Marmayohan, Nivethan, and Abdirahman Farah. "Scene analysis using Tensorflow & YOLO algorithms on Raspberry pi 4." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45540.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Objektdetektion är en av de viktigaste mjukvarukomponenterna i nästa generation trafikövervakning. Deep learnings-algoritmer för objektdetektion, exempelvis YOLO (You Only Look Once), är snabba och noggranna algoritmer i realtid. Realtidsdetektion och igenkänning av objekt är viktiga uppgifter för bildbehandling.  I denna studie presenteras ett inbäddat system för detektion och igenkänning av objekt i normal videohastighet (realtid). Indata är följaktligen en videoström som härstammar från en trafikmiljö i Halmstad. Hårdvaran  är Raspberry pi 4 i vilken programvarupaketen Tensorflow, YOLO  samt  träningskonceptet ”Transfer learning” har implementerats. Resultaten presenteras i form av kvantifiering av realtidskörning på FPS (frames per second), detektion  noggrannhet, CPU-temperatur och CPU-frekvens i olika experiment. En slutsats är att Raspberry pi 4 kan utföra objektklassificering och detektion med hög noggrannhet i en del scenarier för trafikövervakning med YOLO-algoritmer. Ett scenario för att klassificera objekt med långsam hastighet till exempel gående, skulle det vara genomförbar med att klassificera och detektera med en högnoggrannhet. För objekt med höghastighet som bilar och cyklister så har Raspberry pi 4 svårt att detektera och klassificera objekter.
Object detection is one of the essential software components in the next generation of traffic monitoring. Real-time detection and recognition of objects are essential tasks for image processing. Therefore, deep learning algorithms for object detection such as YOLO (You Only Look Once) are increasingly used in image analysis, since they run in normal video frame rate (real-time)  and are reasonably accurate. This study presents an embedded system and its results for detecting and recognizing objects in real-time. Results are based on a video stream originating from a traffic environment in the city of  Halmstad (Sweden). The embedded system is implemented in Raspberry pi 4 using the software Tensorflow and different deep learning algorithms of the YOLO software package. Real-time analyses on frames per second, accuracy in mean average precision, CPU temperature, and CPU frequency are reported for experiments comprising transfer learning. A main conclusion is that Raspberry pi 4 can perform object classification and detection with high accuracy in certain scenarios for traffic monitoring with YOLO algorithms. For example, classifying objects with the speed of a pedestrian would be feasible with classifying and detecting with high accuracy. On the other hand, with high-speed objects such as cars and cyclists, it is a more challenging task for Raspberry pi 4 to detect and classify objects.
5

Al, Hakim Ezeddin. "3D YOLO: End-to-End 3D Object Detection Using Point Clouds." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234242.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
For safe and reliable driving, it is essential that an autonomous vehicle can accurately perceive the surrounding environment. Modern sensor technologies used for perception, such as LiDAR and RADAR, deliver a large set of 3D measurement points known as a point cloud. There is a huge need to interpret the point cloud data to detect other road users, such as vehicles and pedestrians. Many research studies have proposed image-based models for 2D object detection. This thesis takes it a step further and aims to develop a LiDAR-based 3D object detection model that operates in real-time, with emphasis on autonomous driving scenarios. We propose 3D YOLO, an extension of YOLO (You Only Look Once), which is one of the fastest state-of-the-art 2D object detectors for images. The proposed model takes point cloud data as input and outputs 3D bounding boxes with class scores in real-time. Most of the existing 3D object detectors use hand-crafted features, while our model follows the end-to-end learning fashion, which removes manual feature engineering. 3D YOLO pipeline consists of two networks: (a) Feature Learning Network, an artificial neural network that transforms the input point cloud to a new feature space; (b) 3DNet, a novel convolutional neural network architecture based on YOLO that learns the shape description of the objects. Our experiments on the KITTI dataset shows that the 3D YOLO has high accuracy and outperforms the state-of-the-art LiDAR-based models in efficiency. This makes it a suitable candidate for deployment in autonomous vehicles.
För att autonoma fordon ska ha en god uppfattning av sin omgivning används moderna sensorer som LiDAR och RADAR. Dessa genererar en stor mängd 3-dimensionella datapunkter som kallas point clouds. Inom utvecklingen av autonoma fordon finns det ett stort behov av att tolka LiDAR-data samt klassificera medtrafikanter. Ett stort antal studier har gjorts om 2D-objektdetektering som analyserar bilder för att upptäcka fordon, men vi är intresserade av 3D-objektdetektering med hjälp av endast LiDAR data. Därför introducerar vi modellen 3D YOLO, som bygger på YOLO (You Only Look Once), som är en av de snabbaste state-of-the-art modellerna inom 2D-objektdetektering för bilder. 3D YOLO tar in ett point cloud och producerar 3D lådor som markerar de olika objekten samt anger objektets kategori. Vi har tränat och evaluerat modellen med den publika träningsdatan KITTI. Våra resultat visar att 3D YOLO är snabbare än dagens state-of-the-art LiDAR-baserade modeller med en hög träffsäkerhet. Detta gör den till en god kandidat för kunna användas av autonoma fordon.
6

Yevsieiev, V., O. Tokarieva, and S. Starikova. "Research of Object Recognition in the Workspace of A Mobile Robot Based on the Yolo Method." Thesis, Кременчуцький національний університет імені Михайла Остроградського, 2022. https://openarchive.nure.ua/handle/document/20421.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
One of the hallmarks of the advent of the new industrial revolution, Industry 5.0, is the synergy between autonomous robots and humans. All this is possible with the introduction of collaborative robots into all spheres of human activity.
7

Pini, Mattia. "Sviluppo di un Prototipo di Video Sorveglianza Indoor con Tecnologie YOLO ed OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18178/.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
In questa tesi viene effettuato uno studio sulla reale efficienza delle Reti Neurali Artificiali e più in particolare delle Convolutional Neural Network nel settore del riconoscimento immagini e della velocità con cui è possibile ottenere informazioni dettagliate da esse. Viene presentato quindi un Prototipo di Video Sorveglianza per Ambienti Indoor che sfrutta queste tecnologie in maniera efficiente coniugando lo stato dell'arte per il riconoscimento degli oggetti con algoritmi noti per il riconoscimento facciale. Con un attento studio si è dimostrato inoltre perchè la rete scelta è risultata ottimale per l'obiettivo della tesi, mostrandone peculiarità e prestazioni. Vengono inoltre affrontate le problematiche che gli algoritmi di riconoscimento facciale classici possiedono, di come l'inclinazione di un volto possa portare a un non riconoscimento della figura dello stesso, e le soluzioni proposte. Importante inoltre la scelta del dataset per il riconoscimento facciale, poichè una scelta errata potrebbe portare a falsi positivi. Il risultato è un sistema che attraverso acquisizione immagini da videocamera e attraverso l'uso di reti neurali riesce a verificare se un determinato oggetto o una persona sono realmente presenti o meno all'interno di un ambiente.
8

Sommer, Ted Robert. "The aquatic ecology of the Yolo Bypass floodplain : evaluation at the species and landscape scales /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Головатий, Ігор Богданович, and Ihor Holovatiy. "Комп'ютерна система на основі нейромережі для виявлення зіткнення автомобілів." Bachelor's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/35429.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Кваліфікаційна робота присвячена розробці системи, що дозволяє визначати серйозні автомобільні зіткнення на відеоряді, записаному камерами дорожнього спостереження. Проведено огляд існуючих систем детектування дорожньо-транспортних пригод. Запропоновано спосіб вирішення проблеми за допомогою нейромережі, Наведено опис алгоритму детектування автокатастроф, здійснено пошук і підготовка вибірки. Проаналізовано алгоритми детектування об'єктів. Для детектування автомобіля на відео вибрано YOLOv3-детектор. Здійснено порівняння детекторів об'єктів для з'ясування впливу на кінцеву ефективність роботи системи в цілому. Реалізовано алгоритм детектування зіткнення автомобілів в режимі реального часу. Розроблювана система була протестована на реальних даних для визначення зіткнень автомобілів. Отримані практичні результати дозволяють стверджувати про ефективність використання розробки..
The qualification work deals with the development of a system that allows you to identify serious car collisions on a video recorded by surveillance cameras. A review of existing road accident detection systems was conducted. The way of the decision of a problem by means of a neural network is offered, the description of algorithm of detection of car accidents is given, search and preparation of sampling is carried out. Object detection algorithms are analyzed. A YOLOv3 detector is selected to detect the car on video. The object detectors are compared to determine the impact on the final efficiency of the system as a whole. The algorithm of car collision detection in real time is implemented. The developed system was tested on real data to determine car collisions. The obtained practical results allow us to assert the effectiveness of the development.
Вступ. 1. Аналіз технічного завдання. 1.1 Огляд систем детектування ДТП. 1.2. Поняття ДТП. 2. Проектна частина. 2.1. Згорткові нейронні мережі. 2.2. Спосіб вирішення проблеми. за допомогою згорткових нейронних мереж. 2.3. Опис алгоритму детектування автокатастроф. 2.4. Пошук і підготовка вибірки. 2.5. Аналіз алгоритмів детектування об'єктів. 3. Практична частина. 3.1. Порівняння детекторів об'єктів для з'ясування впливу на кінцеву ефективність роботи системи в цілому. 3.2. Реалізація алгоритму детектування зіткнення автомобілів в режимі реального часу. 3.3. Отримані результати експериментів. 4. Безпека життєдіяльності, основи хорони праці. Висновки. Список використаних джерел
10

Güven, Jakup. "Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
I detta arbete genomförs utvecklingen av ett realtids objektdetekteringssystem för iOS. För detta ändamål används YOLO, en ett-stegs objektdetekterare och ett s.k. ihoplänkat neuralt nätverk vilket åstadkommer betydligt bättre prestanda än övriga realtidsdetek- terare i termer av hastighet och precision. En dörrdetekterare baserad på YOLO tränas och implementeras i en systemutvecklingsprocess. Maskininlärningsprocessen sammanfat- tas och praxis för att undvika överträning eller “overfitting” samt för att öka precision och hastighet diskuteras och appliceras. Vidare genomförs en rad experiment vilka pekar på att dataaugmentation och inkludering av negativ data i ett dataset medför ökad precision. Hyperparameteroptimisering och kunskapsöverföring pekas även ut som medel för att öka en objektdetekringsmodells prestanda. Författaren lyckas öka modellens mAP, ett sätt att mäta precision för objektdetekterare, från 63.76% till 86.73% utifrån de erfarenheter som dras av experimenten. En modells tendens för överträning utforskas även med resultat som pekar på att träning med över 300 epoker rimligen orsakar en övertränad modell.
This paper features the creation of a real time object detection system for mobile iOS using YOLO, a state-of-the-art one stage object detector and convoluted neural network far surpassing other real time object detectors in speed and accuracy. In this process an object detecting model is trained to detect doors. The machine learning process is outlined and practices to combat overfitting and increasing accuracy and speed are discussed. A series of experiments are conducted, the results of which suggests that data augmentation, including negative data in a dataset, hyperparameter optimisation and transfer learning are viable techniques in improving the performance of an object detection model. The author is able to increase mAP, a measurement of accuracy for object detectors, from 63.76% to 86.73% based on the results of experiments. The tendency for overfitting is also explored and results suggest that training beyond 300 epochs is likely to produce an overfitted model.
More sources

Books on the topic "YOLOX":

1

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wright, Brett. YOLO Juliet. New York: Random House, 2015.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rudolf, Gisela. Yolo: Roman. Frankfurt am Main: Weissbooks.w, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Roggenbuck, Steve, E. E. Scott, and Rachel Younghans. The yolo pages. Brunswick, Maine]: Boost House, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pixabaj, Telma Angelina Can. Jkemiik yoloj li uspanteko =: Gramática uspanteka. Antigua, Guatemala: OKMA, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Coogle, Rachael. Yolo: What will your legacy be? Spokane, WA: Famous Pub., 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "YOLOX":

1

Sun, Jiaze, and Di Luo. "YOLOx-M: Road Small Object Detection Algorithm Based on Improved YOLOx." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 707–16. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_80.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhao, Zhongqi, Qing He, Sixuan Dai, and Qiongshuang Tang. "Improved YOLOX Transmission Line Insulator Identification." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 186–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31733-0_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Song, Shuaibo, Qinjun Zhao, Xuebin Li, and Tao Shen. "Fall Detection Method Based on Improved YOLOX Network." In Lecture Notes in Electrical Engineering, 782–91. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_80.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yu, Minming, Yanjing Lei, Wenyan Shi, Yujie Xu, and Sixian Chan. "An Improved YOLOX for Detection in Urine Sediment Images." In Intelligent Robotics and Applications, 556–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13841-6_50.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chen, Siting, and Dianyong Yu. "Workpiece Detection of Robot Training Platform Based on YOLOX." In Intelligent Robotics and Applications, 689–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13841-6_62.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Poxi, Hua, Wang Chen, and Tang Yu. "Bushing Surface Defect Detection Method Based on Improved YOLOX." In Advanced Manufacturing and Automation XIII, 72–79. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0665-5_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Guangdong, Wenjing Kang, Ruofei Ma, and Like Zhang. "Multi-object Tracking Based on YOLOX and DeepSORT Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 52–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36011-4_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

An, XiangZe, FangYuan Xu, and JianTing Shi. "An Infrared Pedestrian Detection Method Based on Improved YOLOX." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022), 638–48. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_64.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhang, Xin. "Path Planning Based on YOLOX and Improved Dynamic Window Approach." In Lecture Notes in Electrical Engineering, 26–36. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0068-4_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Shan, Shuaidi, Pengpeng Zhang, Xinlei Wang, Shangxian Teng, and Yichen Luo. "Multiple Color Feature and Contextual Attention Mechanism Based on YOLOX." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 137–48. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53404-1_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "YOLOX":

1

Lin, Yusong, Mengdi Liu, Cong Yang, Shuang Li, and Weixing Zhang. "AC-YOLO: A Safety Helmet Detection based on YOLOX." In RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3584376.3584524.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gao, Chang, and Leian Liu. "C-YOLOX: Improved YOLOX Model for Tomato Diseased Leaves Recognition." In 2023 International Seminar on Computer Science and Engineering Technology (SCSET). IEEE, 2023. http://dx.doi.org/10.1109/scset58950.2023.00084.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gu, Zhouyu, YueCheng Yu, Anqi Ning, and Wanye Gu. "YOLOX-Lite: an efficient model based on YOLOX for object detection." In International Conference on Optics and Machine Vision (ICOMV 2023), edited by Jinping Liu. SPIE, 2023. http://dx.doi.org/10.1117/12.2678802.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Zhuang, Song Qiu, Mingsong Chen, Dingding Han, Tiantian Qi, Qingli Li, and Yue Lu. "CCH-YOLOX: Improved YOLOX for Challenging Vehicle Detection from UAV Images." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191242.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Hao, Dezhi Han, Zhongdai Wu, Junxiang Wang, Yuan Fan, and Yachao Zhou. "NAS-YOLOX: ship detection based on improved YOLOX for SAR imagery." In 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2023. http://dx.doi.org/10.1109/cscloud-edgecom58631.2023.00030.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Mingyuan, Bowen Ma, Hao Wang, Yingbo Li, Dongyue Chen, and Tong Jia. "PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX." In 2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2023. http://dx.doi.org/10.1109/cyber59472.2023.10256595.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Guo, Xu, Ming Ma, Jiaqiang Zhang, and Shaojie Li. "YOLOX-B: A Better Yolox Model for Real-Time Driver Behavior Detection." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096629.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Xie, Yunchuan, Shengling Geng, Dan Zhang, Fubo Wang, Yuxiang Wang, and Yuhang Yan. "YOLOX-TE: Remote Sensing Image Object Detection Based on Improved YOLOX-Tiny." In 2023 16th International Conference on Advanced Computer Theory and Engineering (ICACTE). IEEE, 2023. http://dx.doi.org/10.1109/icacte59887.2023.10335461.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Haopeng, Ding, and Yunfei Chen. "Facial acne recognition system based on machine learning." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002832.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Facial acne plagues many people, causing appearance anxiety and even psychological problems. However, the skin detector or software using traditional image processing technology on the market cannot give consideration to both low cost and high precision. This research aims to develop a low-cost and efficient method to detect facial acne through machine learning. We use hundreds of facial acne patients' pictures collected on the network, use Photoshop to split into thousands of pictures of appropriate size and manually label them as data sets and verification sets, and train them in YOLOX model to finally identify and label skin problems such as facial pustules, acne marks, etc. through one person's facial photos. At present, we have run the system on the desktop (AMD R7 4800H+GTX1650) normally, using the latest YOLOX framework of the open-source YOLO series. In order to improve the learning quality under limited training data, image preprocessing including sharpening and flipping is introduced. The experimental results show that the recognition rate of this method for some skin problems can reach 80%. By further expanding the data set, it can achieve low-cost facial problem recognition. At the same time, this research is also a good case of applying deep learning technology to product design.
10

Shi, Yuning, and Akinori Hidaka. "Attention-YOLOX: Improvement in On-Road Object Detection by Introducing Attention Mechanisms to YOLOX." In 2022 International Symposium on Computing and Artificial Intelligence (ISCAI). IEEE, 2022. http://dx.doi.org/10.1109/iscai58869.2022.00012.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "YOLOX":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
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.
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Geologic map and map database of northeastern San Francisco Bay region, California, [including] most of Solano County and parts of Napa, Marin, Contra Costa, San Joaquin, Sacramento, Yolo, and Sonoma Counties. US Geological Survey, 2002. http://dx.doi.org/10.3133/mf2403.

Full text
APA, Harvard, Vancouver, ISO, and other styles

To the bibliography