改进YOLOv5的安全帽佩戴检测算法

Helmet Wearing Detection Algorithm by Improved YOLOv5

近年来,安全帽佩戴检测在深度学习领域得到广泛研究,为解决现有安全帽检测算法精度低、抗干扰能力弱、移植性较差等问题,提出一种基于改进YOLOv5的目标检测算法。首先,引入残差模块将深层特征信息与浅层特征信息相连,加强网络间的信息交流;然后结合坐标注意力机制(CA),在不增加计算量的前提下提升模型特征提取能力;最后优化损失函数曲线,以提升模型回归效果。实验表明,所提模型相较于传统YOLOv5方法检测精度提升3.6%,能快速准确地检测安全帽,更适合解决实际工程问题。

In recent years,helmet wearing detection has been widely studied in the field of deep learning.In order to solve the problems of low accuracy,weak anti-interference ability,and poor portability of existing helmet detection algorithms,an improved YOLOv5 based object detection algorithm is proposed.Firstly,the residual module is introduced to connect deep feature information with shallow feature informa⁃tion,enhancing information exchange between networks;Then,combined with the coordinate attention mechanism(CA),the model's feature extraction ability is improved without increasing computational complexity;Finally,optimize the loss function curve to improve the regression effect of the model.The experiment shows that the proposed model improves the detection accuracy by 3.6%compared to the traditional YO⁃LOv5 method,and can quickly and accurately detect safety helmets,making it more suitable for solving practical engineering problems.