Abstract
This lookup focuses on object detection the use of the deep mastering algorithm YOLOv5, an up-to-date model of YOLO that gives multiplied accuracy and performance. According to the findings, YOLOv5 provides today’s overall performance on the COCO dataset, with a precision rating of 0.709, a recall rating of 0.634, and with MAP50 (mean average precision) 0.713. The learn about demonstrates the doable of YOLOv5 for sensible applications, inclusive of independent vehicles, security, and surveillance. In this research, we inspect the utilization of YOLOv5 deep mastering strategies for object detection.
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References
Sowjanya Vuddanti JS, Ramesh J (2022) A review based investigation of exploratory analysis in AI and machine learning for a variety of applications. Int J Recent Innov Trends Comput Commun 10(2):182–5. https://doi.org/10.17762/ijritcc.v10i2s.5926
Blaschko MB, Lampert CH (2008) Learning to localize objects with structured output regression. In: Computer Vision–ECCV, Springer, pp 2–15
Wan X (2012) A comparative study of cross-lingual sentiment classification. In: Proceedings of the 2012 IEEE/WIC/ACM international joint conferences on web intelligence and intelligent agent technology, vol 1, pp 24–31, Macau, China
Gu J, Lan C (2019) Joint pedestrian and body part detection via semantic relationship learning app. J Mach Learn Res 9
Uijlings JRR, van de Sande KEA, Gevers T, Smeulders, AWM Selective search for object recognition. Int J Comput Vision 104(2):154–171
Humayun A, Li F, Rehg JMRIGOR (2014) Reusing inference in graph cuts for generating object regions. In: Proceedings of the computer vision and pattern recognition, Columbus, OJ, USA, pp 336–343
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep CNNs. In: Proceedings of the advances in neural information processing systems, Lake Tahoe, NV, USA, pp 1097–1105
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. ar**v preprint ar**v:2004.10934
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Vuddanti, S., Sai Rahul, G., Joel, D.J., Jaswanth, G., Varun, C. (2024). Object Detection the Usage of YOLOV5: A Deep Learning Approach. In: Kumar, A., Mozar, S. (eds) Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering . ICCCE 2024. Lecture Notes in Electrical Engineering, vol 1096. Springer, Singapore. https://doi.org/10.1007/978-981-99-7137-4_60
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DOI: https://doi.org/10.1007/978-981-99-7137-4_60
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