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A review of the development of YOLO object detection algorithm
- 1 South China Normal University
* Author to whom correspondence should be addressed.
Abstract
The You Only Look Once (YOLO) algorithm series, as the forefront of object detection technology, has evolved from YOLOv1 to YOLOv10, consistently enhancing detection speed and accuracy. Through literature review and data analysis. This paper mainly discusses the development processes of the YOLO algorithm series, focuses on the changes and innovations in network structure, training strategies, and performance optimization. By introducing techniques, such as CSPNet, Anchor-free, data augmentation, and multi-scale training, the YOLO algorithm has progressively found a better balance between detection speed and accuracy, demonstrating excellent performance in processing real-time images and handling high-complexity scenarios. Furthermore, this paper also addresses some challenges faced by the YOLO algorithms and potential future research directions, such as the lower accuracy in detecting small targets and reduced robustness in complex scenarios. Through analysis, potential optimization directions for the future include further refining network structure and employing more efficient training methods to enhance algorithm efficiency. This paper intends to offer a thorough performance evaluation of the YOLO series algorithms, identify potential areas for future improvements to advance YOLO technology.
Keywords
YOLO algorithm, object detection, computer vision
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Cite this article
Liang,J. (2024). A review of the development of YOLO object detection algorithm. Applied and Computational Engineering,71,39-46.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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