Face detection based on improved YOLOv5 algorithm

Qiang Jiang; Sihan Chen

doi:10.1117/12.2675124

25 May 2023 Face detection based on improved YOLOv5 algorithm

Qiang Jiang, Sihan Chen

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263617 (2023) https://doi.org/10.1117/12.2675124
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Aiming at the problem that the traditional convolutional neural network has low accuracy face detection in complex scenes, an improved YOLOv5 lightweight network algorithm is proposed. By adding Swin Transformer Block backbone network, the window scheduling algorithm is implemented by using a moving Windows, which limits the self-attention calculation mechanism to non-overlapping local Windows. Meanwhile, the sensitivity field is increased under the condition of allowing cross-window connections to achieve better feature extraction. In order to verify the superiority of Swin-Transformer-YOLOv5, the model is compared with the traditional YOLOv5 algorithm. The model uses a WIDER face in complex scenarios to expose the dataset. The simulation results show that the algorithm can guarantee the original detection efficiency. Meanwhile, the face detection model based on Swin Transformer YOLOv5 has an average accuracy (mAP) of 82.5%, 4.5 percentage points higher than the traditional YOLOv5 algorithm.

Citation Download Citation

Qiang Jiang and Sihan Chen "Face detection based on improved YOLOv5 algorithm", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263617 (25 May 2023); https://doi.org/10.1117/12.2675124

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KEYWORDS

Transformers

Facial recognition systems

Detection and tracking algorithms

Object detection

Windows

Data modeling

Feature extraction

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