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Improved small-object detection using YOLOv8: A comparative study
- 1 Beijing University of Posts and Telecommunications
- 2 Dongguan University of Technology
- 3 University of Science and Technology of China
- 4 ShanghaiTech University
* Author to whom correspondence should be addressed.
Abstract
In the last decade or so, deep neural networks have evolved at a rapid pace, where computer vision has been constantly refreshing its best performance and has been integrated into our lives. In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. This research aims to optimize the latest YOLOv8 model to improve its detection of small objects and compare it with another different version of YOLO models. To achieve this goal, we used the classical deep learning algorithm YOLOv8 as a benchmark and made several improvements and optimizations. We optimized the definition of the detection head, narrowed its perceptual field, and increased its number, allowing the model to better focus on the detailed information of small objects. We compared the optimized YOLOv8 model with other classical YOLO models, including YOLOv3 and YOLOv5n. The experimental results show that our optimized model improves small object detection with higher accuracy. This research provides an effective solution for small object detection with good application prospects. With the continuous development and improvement of the technology, we believe that the YOLO algorithm will continue to play an essential role in object detection and provide a reliable solution for various real-time applications.
Keywords
object detection, small object, YOLO, detection head
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Cite this article
Huang,H.;Wang,B.;Xiao,J.;Zhu,T. (2024). Improved small-object detection using YOLOv8: A comparative study. Applied and Computational Engineering,41,80-88.
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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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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