基于改进YOLOv5的安全帽佩戴检测算法

针对现有安全帽佩戴检测干扰性强、检测精度低等问题，提出一种基于改进YOLOv5的安全帽检测新算法。首先，针对安全帽尺寸不一的问题，使用K-Means++算法重新设计先验框尺寸并将其匹配到相应的特征层；其次，在特征提取网络中引入多光谱通道注意力模块，使网络能够自主学习每个通道的权重，增强特征间的信息传播，从而加强网络对前景和背景的辨别能力；最后，在训练迭代过程中随机输入不同尺寸的图像，以此增强算法的泛化能力。实验结果表明，在自制安全帽佩戴检测数据集上，所提算法的均值平均精度（mAP）达到96.0%，而对佩戴安全帽的工人的平均精度（AP）达到96.7%，对未佩戴安全帽的工人的AP达到95.2%，相较于YOLOv5算法，该算法对佩戴安全帽的平均检测准确率提升了3.4个百分点，满足施工场景下安全帽佩戴检测的准确率要求。

Safety helmet wearing detection algorithm based on improved YOLOv5

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.