Mask detection algorithm based on the improved YOLOv4 - tiny

Chenhuan Tang; Shiran Zhu; Meng Zhang; Jie Chen; Xingyi Guo

doi:10.1117/12.2671703

28 April 2023 Mask detection algorithm based on the improved YOLOv4 - tiny

Chenhuan Tang, Shiran Zhu, Meng Zhang, Jie Chen, Xingyi Guo

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105S (2023) https://doi.org/10.1117/12.2671703
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Based on YOLOv4-tiny, A lightweight mask detection algorithm is presented. By replacing the CBL module in the backbone feature extraction network (CSPdarknet-tiny) and Yolo Head with Ghost module that reduces the parameters of the network model. By the combination of Ghost module, CBAM attention, SMU activation function, and BN layer, a lightweight attention mechanism residual module (GCS_Block) is designed, which is embedded into the backbone feature extraction network, improving the model extract mask feature level. The Kmeans++ method is used to perform anchor box clustering on the dataset in this thesis. The experimental results show that compared with YOLOv4-tiny, the MAP has increased from 74.02% to 86.77%, the parameter has decreased from 6,056,606 to 1,657,828. The memory size of the model is 5.6MB.

Citation Download Citation

Chenhuan Tang, Shiran Zhu, Meng Zhang, Jie Chen, and Xingyi Guo "Mask detection algorithm based on the improved YOLOv4 - tiny", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105S (28 April 2023); https://doi.org/10.1117/12.2671703

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