Journal of Electronics and Informatics
IRO Journals
Volume 6 — Issue 1 — March 2024

Object Detection using an Advanced Deep Learning Algorithm: YOLOv4

https://doi.org/10.36548/jei.2024.1.002
Poornima R
Electronics and Communication Engineering, SNS College of Technology, Anna University, Coimbatore, India
Thejas S A
Electronics and Communication Engineering, SNS College of Technology, Anna University, Coimbatore, India
Vinith S
Electronics and Communication Engineering, SNS College of Technology, Anna University, Coimbatore, India
Santhosh D
Electronics and Communication Engineering, SNS College of Technology, Anna University, Coimbatore, India
Gowtham R
Electronics and Communication Engineering, SNS College of Technology, Anna University, Coimbatore, India
PDF
PDF

How to Cite

R, Poornima, Thejas S A, Vinith S, Santhosh D, and Gowtham R. 2024. “Object Detection Using an Advanced Deep Learning Algorithm: YOLOv4”. Journal of Electronics and Informatics 6 (1): 14-27. https://doi.org/10.36548/jei.2024.1.002.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— YOLOv4
— Object detection
— TensorFlow
— Deep Learning Algorithm
Published: 03-04-2024

Abstract

This research proposes implementing YOLOv4, a real-time object detection system that can accurately and efficiently recognize objects in photos and videos. The goals include creating the YOLOv4 architecture, generating datasets, training the model, evaluating its performance, deploying it in real-world applications, and providing extensive documentation. Deep learning frameworks such as TensorFlow or PyTorch are used in the implementation, along with advanced techniques such as transfer learning and data augmentation. By curating annotated datasets and refining training techniques, the model hopes to attain high accuracy, precision, and recall in object detection tasks. Performance analysis will compare the model's outcomes to those of cutting-edge systems, assessing its strengths and weaknesses. The deployment phase involves integrating the model into existing systems or creating separate applications for real-world scenarios such as pedestrian and vehicle detection. Comprehensive documentation and a user guide will help developers and users make the best use of the trained model. Overall, this research intends to demonstrate YOLOv4's usefulness and feasibility in developing computer vision technology and supporting the creation of intelligent systems with real-time object identification capabilities.

References

  1. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020).
  2. Lin, Tsung-Yi, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. "Feature pyramid networks for object detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125. 2017.
  3. Huang, Jonathan, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer et al. "Speed/accuracy trade-offs for modern convolutional object detectors." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7310-7311. 2017.
  4. Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems 28 (2015).
  5. Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. "Ssd: Single shot multibox detector." In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21-37. Springer International Publishing, 2016.
  6. Redmon, Joseph, Santosh Divvala, 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.
  7. Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
  8. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-yolov4: Scaling cross stage partial network." In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pp. 13029-13038. 2021.
  9. Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. YOLOv4 tiny: A simplified version for object detection. arXiv preprint arXiv:2004.10934. (2020).
  10. Li, X., Zhang, W., & Shi, J YOLOv4-tiny: A fast and small object detection model for real-time applications. arXiv preprint arXiv:2008.10305. . (2020).
  11. He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.
  12. Chen, Kai, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun et al. "MMDetection: Open mmlab detection toolbox and benchmark." arXiv preprint arXiv:1906.07155 (2019).
  13. Wang, Xiaolong, Ross Girshick, Abhinav Gupta, and Kaiming He. "Non-local neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7794-7803. 2018.
  14. Lin, Tsung-Yi, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. "Focal loss for dense object detection." In Proceedings of the IEEE international conference on computer vision, pp. 2980-2988. 2017.
  15. Liu, Shu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. "Path aggregation network for instance segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759-8768. 2018.
  16. He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
  17. Zhang, Xiangyu, Xinyu Zhou, Mengxiao Lin, and Jian Sun. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848-6856. 2018.