Research on helmet detection algorithm based on improved YOLOv4-tiny

Jianguang Zhao; Zeshan Han; Jingjing Fan; Junqiu Zhang

doi:10.1117/12.2671490

16 March 2023 Research on helmet detection algorithm based on improved YOLOv4-tiny

Jianguang Zhao, Zeshan Han, Jingjing Fan, Junqiu Zhang

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125930F (2023) https://doi.org/10.1117/12.2671490
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

In order to effectively supervise the wearing of safety helmets by construction personnel, the YOLOv4-tiny target detection algorithm is used to detect the wearing of safety helmets. A lightweight model with higher accuracy and less computation is designed for YOLOv4-tiny, which is more suitable for real-time helmet wearing detection. Firstly, G-Resblock is designed to replace Resblock to reduce the computational complexity of the model and occupy less computing resources. However, YOLOv4-tiny is prone to error detection or missed detection in complex work scenarios. In order to solve this problem, an attention mechanism is added to YOLOv4-tiny, the serial channel of CBAM is improved as a parallel channel, and P-CBAM is added to YOLOv4-tiny to solve the problem of poor model detection effect. The improved YOLOv4-tiny can better complete the helmet detection task.

Citation Download Citation

Jianguang Zhao, Zeshan Han, Jingjing Fan, and Junqiu Zhang "Research on helmet detection algorithm based on improved YOLOv4-tiny", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125930F (16 March 2023); https://doi.org/10.1117/12.2671490

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KEYWORDS

Detection and tracking algorithms

Target detection

Object detection

Safety

Convolution

Data modeling

Small targets

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