Research on real-time detection algorithm of safety helmets in complex operating environment

Tong Xiao, Guodong He, Mingxing Fang, Shaoguo Xie


In order to solve the problems of low detection accuracy when the background of safety helmets is complex at construction sites,
and the safety helmet target is too small to be easily detected, this paper proposes a real-time detection algorithm for safety helmets in complex working environments based on the YOLOv5 framework. An improved YOLOv5 detection algorithm is proposed to address the issues
of missing safety helmets and low detection accuracy in the construction environment. Adding an attention mechanism to the YOLOv5 backbone network, adding a detection layer at the neck of the network, and integrating an ASFF module at the neck of the network have better
detection performance when facing complex backgrounds and dense helmet detection; The experimental results show that compared to the
original YOLOv5 model, the improved average accuracy has increased by 2.4%, reaching 91.3%, effectively improving the detection ability
of safety helmets in complex environments.


YOLOv5s; CoordAtt; ASFF: henlmet

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