Aiming at the problems of strong interference and low detection precision of the existing safety helmet wearing detection， an algorithm of safety helmet detection based on improved YOLOv5 （You Only Look Once version 5） model was proposed. Firstly， for the problem of different sizes of safety helmets， the K-Means++ algorithm was used to redesign the size of the anchor box and match it to the corresponding feature layer. Secondly， the multi-spectral channel attention module was embedded in the feature extraction network to ensure that the network was able to learn the weight of each channel autonomously and enhance the information dissemination between the features， thereby strengthening the network ability to distinguish foreground and background. Finally， images of different sizes were input randomly during the training iteration process to enhance the generalization ability of the algorithm. Experimental results show as follows： on the self-built safety helmet wearing detection dataset， the proposed algorithm has the mean Average Precision （mAP） reached 96.0%， the the Average Precision （AP） of workers wearing safety helmet reached 96.7%， and AP of workers without safety helmet reached 95.2%. Compared with the YOLOv5 algorithm， the proposed algorithm has the mAP of helmet safety-wearing detection increased by 3.4 percentage points， and it meets the accuracy requirement of helmet safety-wearing detection in construction scenarios.