Abstract:  A protective equipment detection method based on CAS-YOLOv5 is proposed to address the issues of missed detection， positioning errors， and low accuracy in the detection of helmets and safety vests in complex environments using existing algorithms. Firstly， in order to solve the problem of missed detection of small targets， the ASFF（Adaptively Spatial Feature Fusion） detection head is used to improve the model’s recognition ability for small targets. Secondly， in order to improve the detection accuracy of the model and correct positioning errors， a coordinate attention mechanism is added to the backbone network to enhance the model’s perception of important target areas and improve the recall rate of target detection. Once again， we use the WIoU loss function to accelerate the convergence speed of model training， and add Slim-Neck composed of GSConv（Group Shuffle Convolution） modules at the network neck to reduce the dimensionality of feature maps and improve the computational efficiency of the model. Finally， through ablation and comparative experiments on a public dataset， the mAP index of this method  is improved by 5.6 percentage points， and the recall rate is increased by 4 percentage points compared to the YOLOv5 model. The improved method can reduce the missed detection rate and effectively improve detection performance， which has good application prospects in construetion protective equipent detection.

Key words: equipment detection, loss function, GSConv, ASFF