Готові списки джерел за темами / YOLO method

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Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "YOLO method"

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Wu, Wentong, Han Liu, Lingling Li, Yilin Long, Xiaodong Wang, Zhuohua Wang, Jinglun Li, and Yi Chang. "Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image." PLOS ONE 16, no. 10 (October 29, 2021): e0259283. http://dx.doi.org/10.1371/journal.pone.0259283.

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This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.
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Liu, Chunsheng, Yu Guo, Shuang Li, and Faliang Chang. "ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images." Sensors 19, no. 12 (June 13, 2019): 2671. http://dx.doi.org/10.3390/s19122671.

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You Only Look Once (YOLO) deep network can detect objects quickly with high precision and has been successfully applied in many detection problems. The main shortcoming of YOLO network is that YOLO network usually cannot achieve high precision when dealing with small-size object detection in high resolution images. To overcome this problem, we propose an effective region proposal extraction method for YOLO network to constitute an entire detection structure named ACF-PR-YOLO, and take the cyclist detection problem to show our methods. Instead of directly using the generated region proposals for classification or regression like most region proposal methods do, we generate large-size potential regions containing objects for the following deep network. The proposed ACF-PR-YOLO structure includes three main parts. Firstly, a region proposal extraction method based on aggregated channel feature (ACF) is proposed, called ACF based region proposal (ACF-PR) method. In ACF-PR, ACF is firstly utilized to fast extract candidates and then a bounding boxes merging and extending method is designed to merge the bounding boxes into correct region proposals for the following YOLO net. Secondly, we design suitable YOLO net for fine detection in the region proposals generated by ACF-PR. Lastly, we design a post-processing step, in which the results of YOLO net are mapped into the original image outputting the detection and localization results. Experiments performed on the Tsinghua-Daimler Cyclist Benchmark with high resolution images and complex scenes show that the proposed method outperforms the other tested representative detection methods in average precision, and that it outperforms YOLOv3 by 13.69 % average precision and outperforms SSD by 25.27 % average precision.
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Huang, Shan, Ye He, and Xiao-an Chen. "M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012094. http://dx.doi.org/10.1088/1742-6596/1883/1/012094.

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Li, Xun, Yao Liu, Zhengfan Zhao, Yue Zhang, and Li He. "A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video." Journal of Advanced Transportation 2018 (November 4, 2018): 1–11. http://dx.doi.org/10.1155/2018/7075814.

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Анотація:
Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.
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Daniels, Steve, Nanik Suciati, and Chastine Fathichah. "Indonesian Sign Language Recognition using YOLO Method." IOP Conference Series: Materials Science and Engineering 1077, no. 1 (February 1, 2021): 012029. http://dx.doi.org/10.1088/1757-899x/1077/1/012029.

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Chen, Wei, Jingfeng Zhang, Biyu Guo, Qingyu Wei, and Zhiyu Zhu. "An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment." Mathematical Problems in Engineering 2021 (October 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/7351470.

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Анотація:
Real-time detection of apples in natural environment is a necessary condition for robots to pick apples automatically, and it is also a key technique for orchard yield prediction and fine management. To make the harvesting robots detect apples quickly and accurately in complex environment, a Des-YOLO v4 algorithm and a detection method of apples are proposed. Compared with the current mainstream detection algorithms, YOLO v4 has better detection performance. However, the complex network structure of YOLO v4 will reduce the picking efficiency of the robot. Therefore, a Des-YOLO structure is proposed, which reduces network parameters and improves the detection speed of the algorithm. In the training phase, the imbalance of positive and negative samples will cause false detection of apples. To solve the above problem, a class loss function based on AP-Loss (Average Precision Loss) is proposed to improve the accuracy of apple recognition. Traditional YOLO algorithm uses NMS (Nonmaximum Suppression) method to filter the prediction boxes, but NMS cannot detect the adjacent apples when they overlap each other. Therefore, Soft-NMS is used instead of NMS to solve the problem of missing detection, so as to improve the generalization of the algorithm. The proposed algorithm is tested on the self-made apple image data set. The results show that Des-YOLO v4 network has ideal features with a mAP (mean Average Precision) of apple detection of 97.13%, a recall rate of 90%, and a detection speed of 51 f/s. Compared with traditional network models such as YOLO v4 and Faster R-CNN, the Des-YOLO v4 can meet the accuracy and speed requirements of apple detection at the same time. Finally, the self-designed apple-harvesting robot is used to carry out the harvesting experiment. The experiment shows that the harvesting time is 8.7 seconds and the successful harvesting rate of the robot is 92.9%. Therefore, the proposed apple detection method has the advantages of higher recognition accuracy and faster recognition speed. It can provide new solutions for apple-harvesting robots and new ideas for smart agriculture.
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Oruganti, Rakesh, and Namratha P. "Cascading Deep Learning Approach for Identifying Facial Expression YOLO Method." ECS Transactions 107, no. 1 (April 24, 2022): 16649–58. http://dx.doi.org/10.1149/10701.16649ecst.

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Face detection is one of the biggest tasks to find things. Identification is usually the first stage of facial recognition. and identity verification. In recent years, in-depth learning algorithms have changed dramatically in object acquisition. These algorithms can usually be divided into two groups, namely two-phase machines like Faster R-CNN or single-phase machines like YOLO. While YOLO and its variants are less accurate than the two-phase detection systems, they outperform other components with wider genes. When faced with standard-sized objects, YOLO works well, but can't get smaller objects. A face recognition system that uses AI (Artificial Intelligence) separates or verifies a person's identity by analyzing their faces. In this project, a single neural network predicts binding boxes and class opportunities directly from the full images in a single test.
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He, Guowen, Wenlong Wang, Bowen Shi, Shijie Liu, Hui Xiang, and Xiaoyuan Wang. "An Improved YOLO v4 Algorithm-based Object Detection Method for Maritime Vessels." International Journal of Science and Engineering Applications 11, no. 04 (April 2022): 50–55. http://dx.doi.org/10.7753/ijsea1104.1001.

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Ship object detection is the core part of the maritime intelligent ship safety assistance technology, which plays a crucial role in ship safety. The object detection algorithm based on the convolutional neural network has greatly improved the accuracy and speed of object detection, which YOLO algorithm stands out among the object detection algorithms with more excellent robustness, detection accuracy, and real-time performance. Based on the YOLO v4 algorithm, this study uses the k-means algorithm to improve clustering at the input side of image data and introduces relevant berth data in the self-organized dataset to achieve detection of ships and berths for the lack of detection of berths in the existing ship detection algorithm. The experimental results show that the mAP and F1-score of the improved YOLO v4 are increased by 2.79% and 0.80%, respectively. The improved YOLO v4 algorithm effectively improves the accuracy of ship object detection, and the in-port berth also achieves better detection results and improves the ship environment perception, which is important in assisting berthing and unberthing.
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Wang, Ying, Jianbo Wu, Hui Deng, and Xianghui Zeng. "Food Image Recognition and Food Safety Detection Method Based on Deep Learning." Computational Intelligence and Neuroscience 2021 (December 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/1268453.

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Анотація:
With the development of machine learning, as a branch of machine learning, deep learning has been applied in many fields such as image recognition, image segmentation, video segmentation, and so on. In recent years, deep learning has also been gradually applied to food recognition. However, in the field of food recognition, the degree of complexity is high, the situation is complex, and the accuracy and speed of recognition are worrying. This paper tries to solve the above problems and proposes a food image recognition method based on neural network. Combining Tiny-YOLO and twin network, this method proposes a two-stage learning mode of YOLO-SIMM and designs two versions of YOLO-SiamV1 and YOLO-SiamV2. Through experiments, this method has a general recognition accuracy. However, there is no need for manual marking, and it has a good development prospect in practical popularization and application. In addition, a method for foreign body detection and recognition in food is proposed. This method can effectively separate foreign body from food by threshold segmentation technology. Experimental results show that this method can effectively distinguish desiccant from foreign matter and achieve the desired effect.
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Huang, Zhijian, Fangmin Li, Xidao Luan, and Zuowei Cai. "A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning." Mathematical Problems in Engineering 2020 (May 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/3510313.

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Анотація:
Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture. Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming. Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL. The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy. Experimental results show that the proposed method can effectively detect mud in ores. More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy.
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Дисертації з теми "YOLO method"

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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.

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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.
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Harper, Nigel Murray. "Comparing the mannitol-egg yolk-polymyxin agar plating method to the three tube most probable number method for enumeration of bacillus cereus spores in raw and high-temperature-short-time pasteurized milk." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1683.

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Gram, Greta. "SUN PIECE : actions of cutting." Thesis, Högskolan i Borås, Institutionen Textilhögskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-17071.

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This works explores how to work with Event scores as a design method. In the search for what is real or what is reality the already existing things are being explored. The work started with investigating suitable ways to work with the moving body in the design process, with the aim to find a method that gave control but also left some parameters to the undecided and ambiguous. Convinced that this will lead to something new some parts of the process were highlighted and re-formulated.
Program: Modedesignutbildningen
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Lucey, Sean M. "Characteristics of fish yolk proteins and a method for inducing vitellogenin." 2009. https://scholarworks.umass.edu/theses/334.

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Teleosts are one of the most diverse groups of vertebrates. They utilize a wide array of reproductive strategies and tactics to overcome the challenges of the many ecological niches they inhabit. The most common reproductive method for teleosts is oviparity. Oviparous animals lay eggs with little or no embryonic development from the mother. The embryos are supplied with nutrition via yolk. Vitellogenesis is the process of the ovary sequestering yolk. It is regulated by exogenous environmental cues that act on the hypothalamus-pituitary-gonad axis. Through a series of hormonal controls, the liver produces the yolk precursor, vitellogenin. Vitellogenin is secreted by the liver and absorbed by the growing oocyte by receptor mediated endocytosis. There it is cleaved into the two main yolk proteins which are subsequently used by the growing embryo. The biggest source of nutrition is the yolk protein lipovitellin which also plays a key role in marine teleosts’ ability to osmoregulate their eggs. Lipovitellin is a large glyco-phospho-lipo-protein ca. 200 kDa. Large proteins usually denature easily. However, prior evidence shows that fish lipovitellins are thermally stable. Using differential scanning calorimetry, I quantify lipovitellin’s thermostability amongst four right-eye flounders (Pleuronectidae: winter flounder, American plaice, witch flounder, and yellowtail flounder). Differential scanning calorimetry allows direct interpretation of all thermodynamic properties; however, Lipovitellin was too large and precipitated before other thermodynamic properties could be determined. Pleuronectid lipovitellins all showed high melting points indicative of high thermostability. This shows that despite differing life histories, lipovitellin is conserved. Presence of the pre-cursor, vitellogenin in male or juvenile fish is used as a biomarker for xenoestrogens, a type of endocrine disrupting chemicals that blocks or mimics natural estrogens. They are known to disrupt aquatic life by interfering with natural development and reproduction. A major biological side effect of xenoestrogens is the accumulation of vitellogenin. This effect has made vitellogenin a useful biomarker for monitoring levels of contamination. Unfortunately, vitellogenin can vary greatly in its immunological and structural characteristics, which means that species-specific assays are necessary. This study took the first step in developing an immunoassay for bluefish (Pomatomus saltatrix). Vitellogenin was induced by injecting a group of bluefish with an estrogen, estradiol, and the resulting vitellogenin was isolated from the serum of males. The protein was characterized as vitellogenin by determining its large Stokes radius in gel permeation chromatography combined with its characteristic peptide molecular weight in sodium dodecyl sulfate polyacrylamide gel electrophoresis.
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Zhang, Yao Ji, and 張耀基. "Pickling conditions and mechanism for producing salted egg yolk using rapid immersion method." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/62281436951878426005.

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Mayombo, Pie Veillard Kalonji. "Evaluation of Nguni bull semen-extended in tris egg yolk extender, soybean milk and coconut water based extenders and stored at different temperatures." Diss., 2017. http://hdl.handle.net/11602/880.

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MSCAGR (Animal Science)
Department of Animal Science
In order to realize many of the potential advantages of AI, storage of semen is necessary. Semen storage is only possible using a system that decreases and/or halts the metabolic processes of the spermatozoa, allowing no significant loss of fertility. Numerous factors affect the success of spermatozoa storage. This study was designed to compare the effects of egg yolk, soybean milk and coconut water in Tris extender using different storage methods for Nguni bull spermatozoa storage. Bull semen was collected from two adult Nguni bulls approximately four years old and kept under similar managerial conditions. Using electro-ejaculator, semen was collected from each bull into a graduated semen collection tube. Macroscopically evaluation of the sample was performed immediately after collection. Only the semen free from contamination was processed. The kinetic properties namely: total spermatozoa motility, and progressive spermatozoa motility were analysed using CASA. Semen sample was stained and spermatozoa morphology and vitality also analysed using CASA. The extended semen was then split into three groups. The first group was stored at room temperature (25 °C). The second group was cooled to 4 °C and stored in the refrigerator. The third group was also cooled to 4 °C for 2 h in the refrigerator, then held in LN2 vapour 5 cm above the surface of LN2 at ~ -80 °C for 10 min and then plunged into LN2 for storage at -196 °C. Different colours of straws and plugging powder were used for identifying each extender. After 3 days of storage at room temperature, in the refrigerator and in LN2, the extended semen was split into three portions and assayed for kinetic properties using the first portion. The second portion was assayed for spermatozoa morphology and the third portion for spermatozoa vitality. The results from the fresh semen extended with all three extenders (TEYE, SBME and COWE), and analysed immediately after dilution at room temperature (25 ºC), showed no significant difference (P > 0.05) in the mean values of the kinetic and morphologic properties and viability, on spermatozoa TM, PM, AR, AT, CT; BT and LS. After three days of storage, there was no significant difference (P > 0.05) in the kinetic morphologic properties and viability of semen stored at room and refrigeration temperature regardless of the extender in use. There were, however, significant differences (P < 0.05) in the TM, PM, AR and DL of the frozen semen samples. For the short storage period of semen used for AI, from this study, it is recommended that semen should be kept at room or refrigeration temperature regardless of the three extenders used. However, for long storage of frozen semen TEYE is recommended. The egg yolk-based extender provided greater preservation of motility and bull spermatozoa integrity during the freezing process than did SBME and COWE.
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Книги з теми "YOLO method"

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Damrosch, David. Comparing the Literatures. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691134994.001.0001.

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Literary studies are being transformed today by the expansive and disruptive forces of globalization. More works than ever circulate worldwide in English and in translation, and even national traditions are increasingly seen in transnational terms. To encompass this expanding literary universe, scholars and teachers need to expand their linguistic and cultural resources, rethink their methods and training, and reconceive the place of literature and criticism in the world. This book integrates comparative, postcolonial, and world-literary perspectives to offer a comprehensive overview of comparative studies and its prospects in a time of great upheaval and great opportunity. The book looks both at institutional forces and at key episodes in the life and work of comparatists who have struggled to define and redefine the terms of literary analysis over the past two centuries, from Johann Gottfried Herder and Germaine de Staël to Edward Said, Gayatri Spivak, Franco Moretti, and Emily Apter. With literary examples ranging from Ovid and Kālidāsa to James Joyce, Yoko Tawada, and the internet artists Young-Hae Chang Heavy Industries, the book shows how the main strands of comparison—philology, literary theory, colonial and postcolonial studies, and the study of world literature—have long been intertwined. A deeper understanding of comparative literature's achievements, persistent contradictions, and even failures can help comparatists in literature and other fields develop creative responses to today's most important questions and debates. Amid a multitude of challenges and new possibilities for comparative literature, the book provides an important road map for the discipline's revitalization.
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Частини книг з теми "YOLO method"

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Zhao, Xia, Yingting Ni, and Haihang Jia. "Modified Object Detection Method Based on YOLO." In Communications in Computer and Information Science, 233–44. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7305-2_21.

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Zheng, Weizhou, and Jiayi Chang. "Helmet Detection Based on an Enhanced YOLO Method." In Lecture Notes in Electrical Engineering, 84–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8599-9_11.

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Zhou, Fangrong, Yi Ma, Yutang Ma, and Hao Pan. "Infrared Image Fault Identification Method Based on YOLO Target Detection Algorithm." In Advances in Intelligent Systems and Computing, 461–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44038-1_42.

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Sasagawa, Yukihiro, and Hajime Nagahara. "YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models." In Computer Vision – ECCV 2020, 345–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58589-1_21.

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Wang, Chen-can, Yan Ge, and Yang Li. "The Method of Anomaly Location Data Recognition Based on Improved YOLO Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 56–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94551-0_5.

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Zhang, Xiufeng, Chen Wang, Changfeng Xiang, Chao Liu, and Yu Li. "Intelligent Detection Method for Welding Seam Defects of Automobile Wheel Hub Based on YOLO." In Lecture Notes in Electrical Engineering, 693–702. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6318-2_86.

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Lopez, Andrew L., Monica D. Garcia, Mary E. Dickinson, and Irina V. Larina. "Live Confocal Microscopy of the Developing Mouse Embryonic Yolk Sac Vasculature." In Methods in Molecular Biology, 163–72. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1462-3_9.

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Ito, Kazumi, Toru Tamura, Noriko Hasebe, Toshio Nakamura, Shoji Arai, Manabu Ogata, Taeko Itono, and Kenji Kashiwaya. "Comparison of Luminescence Dating Methods on Lake Sediments from a Small Catchment: Example from Lake Yogo, Japan." In Earth Surface Processes and Environmental Changes in East Asia, 221–38. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55540-7_11.

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Dua, Mohit, Abhinav Mudgal, Mukesh Bhakar, Priyal Dhiman, and Bhagoti Choudhary. "K-Means and DNN-Based Novel Approach to Human Identification in Low Resolution Thermal Imagery." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, 25–37. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4444-0.ch002.

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Анотація:
In this chapter, a human detection system based on unsupervised learning method K-means clustering followed by deep learning approach You Only Look Once (YOLO) on thermal imagery has been proposed. Generally, images in the visible spectrum are used to conduct such human detection, which are not suitable for nighttime due to low visibility, hence for evaluation of our system. Hence, long wave infrared (LWIR) images have been used to implement the proposed work in this chapter. The system follows a two-step approach of generating anchor boxes using K-means clustering and then using those anchor boxes in 252 layered single shot detector (YOLO) to predict proper boundary boxes. The dataset of such images is provided by FLIR company. The dataset contains 6822 images for training purposes and 757 images for the validation. This proposed system can be used for real-time object detection as YOLO can achieve much higher rate of processing when compared to traditional method like HAAR cascade classifier in long wave infrared imagery (LWIR).
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Wu, Can, and Zhiqiang Zeng. "Spoon Surface Defect Detection Based on Improved YOLO V3." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220021.

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At present, there may be some problems in the production process of spoon, such as the lack of material on the surface of spoon. In order to effectively detect the surface defects of spoon, a defect detection method based on improved YOLO V3 model is proposed in this paper. Firstly, the output layers of the second and third residual blocks in the backbone network Darknet-53 are selected to build the feature pyramid network, which shortens the transmission path of feature information. In this case, we can better retain the feature information of small target defects. Secondly, the anchor boxes is adjusted to strengthen the ability of the model for small target defects detection. We test the proposed method on one spoon defect dataset, which is collected from the real-world industry manufactory scenario. The results show that the average precision of our algorithm reaches at 95.14%, which is better than the conventional YOLO V3 algorithm by 9.35%. Meanwhile, our algorithm is 9.12% faster than YOLO V3 with a 32.3 fps detection speed, demonstrating its efficiency and effectiveness for spoon defect detection.
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Тези доповідей конференцій з теми "YOLO method"

1

Wu, Yachen, Huimin Liu, and Zeyang Miao. "YOLOC: A Vehicle Counting Method for Surveillance Video Based on YOLO." In 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2021. http://dx.doi.org/10.1109/ainit54228.2021.00146.

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Yan, Wei, Ting Liu, and Yuzhuo Fu. "YOLO-Tight: an Efficient Dynamic Compression Method for YOLO Object Detection Networks." In ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3457682.3457740.

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3

Huang, Yilun, Qinqin Yan, Yibo Li, Yifan Chen, Xiong Wang, Liangcai Gao, and Zhi Tang. "A YOLO-Based Table Detection Method." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00135.

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4

Tianjiao, Liang, and Bao Hong. "A optimized YOLO method for object detection." In 2020 16th International Conference on Computational Intelligence and Security (CIS). IEEE, 2020. http://dx.doi.org/10.1109/cis52066.2020.00015.

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5

Peng, Jinmin, Wenyu Liu, Tongfei You, and Binglong Wu. "Improved YOLO-V3 Workpiece Detection Method for Sorting." In 2020 5th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2020. http://dx.doi.org/10.1109/icrae50850.2020.9310804.

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6

Benyang, Deng, Lei Xiaochun, and Ye Miao. "Safety helmet detection method based on YOLO v4." In 2020 16th International Conference on Computational Intelligence and Security (CIS). IEEE, 2020. http://dx.doi.org/10.1109/cis52066.2020.00041.

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7

Huo, Ping, Fang Lv, and Si Chen. "Flame detection method based on improved YOLO-v3." In International Conference on Signal Image Processing and Communication (ICSIPC 2021), edited by Siting Chen and Wei Qin. SPIE, 2021. http://dx.doi.org/10.1117/12.2600353.

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8

Yang, Yong-Chao, and Wei Chen. "An Improved YOLO Leucocyte Classification and Recognition Method." In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2021. http://dx.doi.org/10.1109/icitbs53129.2021.00157.

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9

Zhou, SuYu, and Jun Yin. "YOLO-Ship: A Visible Light Ship Detection Method." In 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2022. http://dx.doi.org/10.1109/iccece54139.2022.9712768.

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10

Lina, Wei, and Jiangtao Ding. "Behavior detection method of OpenPose combined with Yolo network." In 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2020. http://dx.doi.org/10.1109/cisce50729.2020.00072.

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Звіти організацій з теми "YOLO method"

1

Wong, Eric A., and Zehava Uni. Nutrition of the Developing Chick Embryo: Nutrient Uptake Systems of the Yolk Sac Membrane and Embryonic Intestine. United States Department of Agriculture, June 2012. http://dx.doi.org/10.32747/2012.7697119.bard.

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We have examined the developmental changes in composition, amount, and uptake of yolk nutrients (fat, protein, water and carbohydrates) and the expression ofnutrient transporters in the yolk sac membrane (YSM) from embryonic day 11 (Ell) to 21 (E21) and small intestine from embryonic day 15 (E15) to E21 in embryos from young (22-25 wk) and old (45-50 wk) Cobb and Leghorn breeder flocks. The developmental expression profiles for the peptide transporter 1 (PepTl), the amino acid transporters, EAAT3, CAT-1 and BOAT, the sodium glucose transporter (SGLTl), the fructose transporter (GLUT5), the digestive enzymes aminopeptidase N (APN) and sucraseisomaltase (SI) were assayed by the absolute quantification real time PCR method in the YSM and embryonic intestine. Different temporal patterns of expression were observed for these genes. The effect of in ovo injection of peptides (the dipeptide Gly-Sar, purified peptides, trypsin hydrolysate) on transporter gene expression has been examined in the embryonic intestine. Injection of a partial protein hydrolysate resulted in an increase in expression of the peptide transporter PepT2. We have initiated a transcriptome analysis of genes expressed in the YSM at different developmental ages to better understand the function of the YSM.
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Uni, Zehava, and Peter Ferket. Enhancement of development of broilers and poults by in ovo feeding. United States Department of Agriculture, May 2006. http://dx.doi.org/10.32747/2006.7695878.bard.

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The specific objectives of this research were the study of the physical and nutritional properties of the In Ovo Feeding (IOF) solution (i.e. theosmostic properties and the carbohydrate: protein ratio composition). Then, using the optimal solution for determining its effect on hatchability, early nutritional status and intestinal development of broilers and turkey during the last quarter of incubation through to 7 days post-hatch (i.e. pre-post hatch period) by using molecular, biochemical and histological tools. The objective for the last research phase was the determination of the effect of in ovo feeding on growth performance and economically valuable production traits of broiler and turkey flocks reared under practical commercial conditions. The few days before- and- after hatch is a critical period for the development and survival of commercial broilers and turkeys. During this period chicks make the metabolic and physiological transition from egg nutriture (i.e. yolk) to exogenous feed. Late-term embryos and hatchlings may suffer a low glycogen status, especially when oxygen availability to the embryo is limited by low egg conductance or poor incubator ventilation. Much of the glycogen reserve in the late-term chicken embryo is utilized for hatching. Subsequently, the chick must rebuild that glycogen reserve by gluconeogenesis from body protein (mostly from the breast muscle) to support post-hatch thermoregulation and survival until the chicks are able to consume and utilize dietary nutrients. Immediately post-hatch, the chick draws from its limited body reserves and undergoes rapid physical and functional development of the gastrointestinal tract (GIT) in order to digest feed and assimilate nutrients. Because the intestine is the nutrient primary supply organ, the sooner it achieves this functional capacity, the sooner the young bird can utilize dietary nutrients and efficiently grow at its genetic potential and resist infectious and metabolic disease. Feeding the embryo when they consume the amniotic fluid (IOF idea and method) showed accelerated enteric development and elevated capacity to digest nutrients. By injecting a feeding solution into the embryonic amnion, the embryo naturally consume supplemental nutrients orally before hatching. This stimulates intestinal development to start earlier as was exhibited by elevated gene expression of several functional genes (brush border enzymes an transporters , elvated surface area, elevated mucin production . Moreover, supplying supplemental nutrients at a critical developmental stage by this in ovo feeding technology improves the hatchling’s nutritional status. In comparison to controls, administration of 1 ml of in ovo feeding solution, containing dextrin, maltose, sucrose and amino acids, into the amnion of the broiler embryo increased dramatically total liver glycogen in broilers and in turkeys in the pre-hatch period. In addition, an elevated relative breast muscle size (% of broiler BW) was observed in IOF chicks to be 6.5% greater at hatch and 7 days post-hatch in comparison to controls. Experiment have shown that IOF broilers and turkeys increased hatchling weights by 3% to 7% (P<0.05) over non injected controls. These responses depend upon the strain, the breeder hen age and in ovo feed composition. The weight advantage observed during the first week after hatch was found to be sustained at least through 35 days of age. Currently, research is done in order to adopt the knowledge for commercial practice.
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