Academic literature on the topic 'YOLO method'
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Journal articles on the topic "YOLO method"
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.
Full textLiu, 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.
Full textHuang, 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.
Full textLi, 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.
Full textDaniels, 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.
Full textChen, 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.
Full textOruganti, 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.
Full textHe, 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.
Full textWang, 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.
Full textHuang, 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.
Full textDissertations / Theses on the topic "YOLO method"
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 textHarper, 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.
Full textGram, 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.
Full textProgram: Modedesignutbildningen
Lucey, Sean M. "Characteristics of fish yolk proteins and a method for inducing vitellogenin." 2009. https://scholarworks.umass.edu/theses/334.
Full textZhang, 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.
Full textMayombo, 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.
Full textDepartment 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.
Books on the topic "YOLO method"
Damrosch, David. Comparing the Literatures. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691134994.001.0001.
Full textBook chapters on the topic "YOLO method"
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.
Full textZheng, 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.
Full textZhou, 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.
Full textSasagawa, 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.
Full textWang, 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.
Full textZhang, 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.
Full textLopez, 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.
Full textIto, 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.
Full textDua, 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.
Full textWu, 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.
Full textConference papers on the topic "YOLO method"
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.
Full textYan, 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.
Full textHuang, 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.
Full textTianjiao, 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.
Full textPeng, 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.
Full textBenyang, 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.
Full textHuo, 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.
Full textYang, 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.
Full textZhou, 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.
Full textLina, 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.
Full textReports on the topic "YOLO method"
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.
Full textUni, 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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