The application and research of improved YOLOv10 algorithm in smoking behavior detection on campus

Li Lu

doi:10.1117/12.3050443

24 October 2024 The application and research of improved YOLOv10 algorithm in smoking behavior detection on campus

Li Lu

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960U (2024) https://doi.org/10.1117/12.3050443
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

In order to maintain a good living and learning environment and students' health in campus, it is particularly important to detect students' smoking behavior in campus. With the continuous development of artificial intelligence, object detection algorithms based on computer vision are becoming increasingly perfect. However, due to the small size of the cigarette target, existing object detection algorithms have serious missed detection and false detection phenomena, and are difficult to use in a real campus environment. Based on the YOLOv10 object detection algorithm, this paper proposes a multi-level end-to-end detection model M-YOLOv10. Aiming at the limited feature extraction capabilities of the original YOLOv10, a self-attention mechanism is introduced to improve the model's global feature extraction capabilities. At the same time, the feature fusion module is improved to achieve multi-level fusion and improve the model's small object detection capabilities. Experimental results show that compared with the model before improvement, the M-YOLOv10 algorithm improves mAP@0.5 by 17.6%, and the model detection speed remains basically unchanged.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Lu "The application and research of improved YOLOv10 algorithm in smoking behavior detection on campus", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960U (24 October 2024); https://doi.org/10.1117/12.3050443

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