改进的轻量级安全帽佩戴检测算法

Improved lightweight helmet wear detection algorithm

针对现有安全帽佩戴检测算法对密集目标和小目标存在漏检现象且参数多、计算量大,不适合部署在嵌入式设备端等问题,提出了一种改进的YOLOv5安全帽佩戴检测算法YOLOv5-Q。首先,在原网络80×80的特征图上进行2倍上采样操作形成160×160的特征图,新特征图融合了原模型的3层特征信息,形成四尺度检测,提升了密集目标及小目标的检测精度。其次,采用轻量级的GhostNet替换原YOLOv5的主干网络实现特征提取,降低了网络的参数,可以移植在嵌入式设备端实现目标检测。最后,添加注意力机制CA提升特征图中重要信息的权重,抑制非相关信息的权重,从而提升模型的精度。实验结果表明,YOLOv5-Q的模型大小为26.47 MB,参数量为12696640,精度为0.937。与YOLOv5相比,YOLOv5-Q算法的参数量减少了39.12%,模型大小降低了37.2%,但是精度仅降低了1.2%。YOLOv5-Q算法提高了密集环境下小目标的检测精度且满足在嵌入式端部署的需求。

For the existing helmet wearing detection algorithm for dense targets and small targets having the phenomenon of missed detection,many parameters,large computation,and not suitable for deployment in the embedded device side and other problems,this paper proposes an improved YOLOv5 helmet wearing detection algorithm YOLOv5-Q.Firstly,on the original network 80×80 feature map,the 2 times up-sampling operation is made to form a 160×160 feature map,and the new feature map fuses the three-layer feature information of the original model to form a four-scale detection,which improves the detection accuracy of dense targets and small targets.Secondly,the feature extraction is achieved by replacing the original YOLOv5 backbone network with a lightweight GhostNet,which reduces the parameters of the network and can be ported to the embedded devices for target detection.Finally,the attention mechanism CA is added to boost the weight of important information in the feature map and suppress the weight of non-relevant information,thus improving the accuracy of the model.The experimental results show that the model size of YOLOv5-Q is 26.47 MB,the number of parameters is 12696640,and the accuracy is 0.937.Compared with YOLOv5,the YOLOv5-Q algorithm reduces the number of parameters by 39.12%,the model size by 37.2%,but the accuracy by only 1.2%.The YOLOv5-Q algorithm increases the detection accuracy of small targets in dense environments and meets the requirements for deployment on the embedded side.