Enhancing Real-time Object Detection with YOLO Algorithm
DOI:
https://doi.org/10.4108/eetiot.4541Keywords:
computer vision, image processing, object detection, CNN, accuracyAbstract
This paper introduces YOLO, the best approach to object detection. Real-time detection plays a significant role in various domains like video surveillance, computer vision, autonomous driving and the operation of robots. YOLO algorithm has emerged as a well-liked and structured solution for real-time object detection due to its ability to detect items in one operation through the neural network. This research article seeks to lay out an extensive understanding of the defined Yolo algorithm, its architecture, and its impact on real-time object detection. This detection will be identified as a regression problem by frame object detection to spatially separated bounding boxes. Tasks like recognition, detection, localization, or finding widespread applicability in the best real-world scenarios, make object detection a crucial subdivision of computer vision. This algorithm detects objects in real-time using convolutional neural networks (CNN). Overall this research paper serves as a comprehensive guide to understanding the detection of objects in real-time using the You Only Look Once (YOLO) algorithm. By examining architecture, variations, and implementation details the reader can gain an understanding of YOLO’s capability.
Downloads
References
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection. "Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
W. Zhiqiang, L. Jun, A review of object detection based on convolutional neural network, in: 2017 36th Chinese Control Conference (CCC), 2017, pp. 11104– 11109. doi: 10.23919/ChiCC.2017.8029130 DOI: https://doi.org/10.23919/ChiCC.2017.8029130
Arya MC, Rawat A. A review on YOLO (You Look Only One)-an algorithm for real time object detection. J Eng Sci. 2020;11:554-7
Arya, Mukesh Chandra, and Anchal Rawat. "A review on YOLO (You Look Only One)-an algorithm for real time object detection." J Eng Sci 11 (2020): 554-7.
Redmon et al. in You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Tsang, Sik-Ho. "Review: YOLOv2 & YOLO9000—You Only Look Once (Object Detection (accessed on 24 February 2019) (2019 )u Only Look Once: Unified, Real-Time Object Detection
M. Takahashi, Y. Ji, K. Umeda and A. Moro, "Expandable YOLO: 3D Object Detection from RGB-D Images," 2020 21st International Conference on Research and Education in Mechatronics (REM), 2020, pp. 1-5, doi: 10.1109/REM49740.2020.9313886. DOI: https://doi.org/10.1109/REM49740.2020.9313886
Ju, M.; Luo, H.; Wang, Z.; Hui, B.; Chang, Z. The Application of Improved YOLO V3 in Multi-Scale Target Detection. Appl. Sci. 2019, 9, 3775. DOI: https://doi.org/10.3390/app9183775
Wei Fang 1,2, (member, ieee), Lin Wang 1 , and Peiming Ren 1, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments.
Yi, Zhang, Shen Yongliang, and Zhang Jun. "An improved tiny-yolov3 pedestrian detection algorithm." Optik 183 (2019): 17-23.FANG 1,2, (Member, IEEE), lin wang 1 , and peiming ren 1, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments. DOI: https://doi.org/10.1016/j.ijleo.2019.02.038
Tian, Yunong, et al. "Apple detection during different growth stages in orchards using the improved YOLO-V3 model." Computers and electronics in agriculture 157 (2019): 417-426 DOI: https://doi.org/10.1016/j.compag.2019.01.012
Shafiee, Mohammad Javad, et al. "Fast YOLO: A fast you only look once system for real-time embedded object detection in video." arXiv preprint arXiv:1709.05943 (2017). DOI: https://doi.org/10.15353/vsnl.v3i1.171
George, Jose, Shibon Skaria, and V. V. Varun. "Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans." Medical Imaging 2018: Computer-Aided Diagnosis. Vol. 10575. SPIE, 2018.mad Javad, et al. "Fast YOLO: A fast you only look once system for real-time embedded object detection in video." arXiv preprint arXiv:1709.05943 (2017).
Chiang, Holly, Yifan Ge, and Connie Wu. "Multiple Object Recognition with Focusing and Blurring." Lectures from the Course (2016).
Du, Juan. "Understanding of object detection based on CNN family and YOLO." Journal of Physics: Conference Series. Vol. 1004. No. 1. IOP Publishing, 2018 DOI: https://doi.org/10.1088/1742-6596/1004/1/012029
Joseph Redmon∗, Santosh Divvala, Ross Girshick Ali Farhadi∗ University of Washington∗, Allen Institute for AI†, Facebook AI Research, ”You Only Look Once: Unified, Real-Time Object Detection”
Santosh Divvala, Redmon, Joseph, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788. 2016
Wong, Alexander, et al. "Yolo nano: a highly compact you only look once convolutional neural network for object detection." 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing-NeurIPS Edition (EMC2-NIPS). IEEE, 2019 DOI: https://doi.org/10.1109/EMC2-NIPS53020.2019.00013
Wei H, Kehtarnavaz N (2019) Semi-supervised faster RCNN-based person detection and load classification for far field video surveillance. Mach Learn Knowl Extraction 1(3):756–767 DOI: https://doi.org/10.3390/make1030044
Tsang S-H (2018) Review: Inception-v4 - Evolved From GoogLeNet, Merged with ResNet Idea (Image Classification), towards data science
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
H. Deshpande , A. Singh, H. Herunde, “Comparative Analysis on YOLO Object Detection with OpenCV”
Stauffer, C., & Grimson, W. E. L. (1999, June). Adaptive background mixture models for real-time tracking. Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149) (Vol. 2, pp. 246-252). IEEE
Liu, Y., Ai, H., & Xu, G. Y. (2001, September). Moving object detection and tracking based on background subtraction. Proc. SPIE 4554, object detection, classification, and tracking technologies (Vol. 4554, pp. 62-66). DOI: https://doi.org/10.1117/12.441618
Sungandi, B., Kim, H., Tan, J. K., & Ishikawa, S. (2009). Real time tracking and identification of moving persons by using a camera in outdoor environment. International journal of innovative computing, information and control, 5, 1179-1188
Jacques, J. C. S., Jung, C. R., & Musse, S. R. (2005, October). Background subtraction and shadow detection in grayscale video sequences. XVIII Brazilian symposium on computer graphics and image processing (SIBGRAPI'05) (pp. 189-196). IEEE DOI: https://doi.org/10.1109/SIBGRAPI.2005.15
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 DOI: https://doi.org/10.1109/CVPR.2016.91
Long, Xiang, et al. "PP-YOLO: An effective and efficient implementation of object detector." arXiv preprint arXiv:2007.12099 (2020)
Zhang, Zhi, et al. "Bag of freebies for training object detection neural networks." arXiv preprint arXiv:1902.04103 (2019)
Yin, Xuanyu, et al. "YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud." The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019. The Japan Society of Mechanical Engineers, 2019 DOI: https://doi.org/10.1299/jsmermd.2019.2P1-I01
Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI: https://doi.org/10.1109/CVPR.2017.690
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097-1105).
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
