Lightweight hand small target detection method based on improved YOLOv5s

Changsheng Liu; Xuejun Zhang

doi:10.1117/12.3037076

22 October 2024 Lightweight hand small target detection method based on improved YOLOv5s

Changsheng Liu, Xuejun Zhang

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740C (2024) https://doi.org/10.1117/12.3037076
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

In response to the issues of low frames per second and small gesture scale in current hand interaction detection leading to accuracy problems in hand detection, we propose a lightweight small target hand recognition method - RB-YOLOv5s. This method first solves the problem of excessive parameter quantity and low recognition accuracy by replacing the Conv module in YOLOv5s with the RepVGGBlock module and reducing the number of modules. Secondly, a bidirectional feature pyramid structure is introduced in the feature fusion network to enhance the degree of semantic information and location information fusion. Finally, the CIOU loss function in YOLOv5s is changed to SIOU to accelerate training speed and efficiency. We validated this method on a public dataset with distant small targets. The experimental results show that the recognition accuracy of our proposed model is 91%, the parameter quantity is only 1.754×10^6, and the Frames Per Second has increased to 104.17.

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

Citation Download Citation

Changsheng Liu and Xuejun Zhang "Lightweight hand small target detection method based on improved YOLOv5s", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740C (22 October 2024); https://doi.org/10.1117/12.3037076

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KEYWORDS

Education and training

Small targets

Convolution

Target detection

Performance modeling

Detection and tracking algorithms

Instrument modeling

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