改进YOLOv5适应安全帽佩戴与口罩佩戴检测应用的算法

An improved algorithm for adapting YOLOv5 to helmet wearing and mask wearing detection applications

为实现自然场景下对安全帽以及口罩佩戴更高效地检测,基于深度学习YOLOv5算法提出了一种改进的算法模型YOLOv5+。对于目标检测任务而言,通常是在较大的特征图上去检测小目标。考虑到所检测对象多为小尺度目标,因此当输入图像尺寸默认为640×640像素时,通过在原算法检测层中增加大小为160×160像素的特征图,并选用CIoU(Complete-IoU)作为损失函数,以实现对安全帽佩戴以及口罩佩戴更有效地检测。实验结果表明,在安全帽佩戴和口罩佩戴数据集上,YOLOv5+网络模型的平均检测精度(mAP-50)分别达到93.8%和92.3%,相比原算法均有所提高。此方法不仅满足了实时性检测的速度要求,同时提高了检测的精度。

In order to achieve more efficient detection of wearing helmets and masks in natural scenes,an improved algorithm model YOLOv5+is proposed based on the deep learning algorithm YOLOv5.For target detection tasks,small targets are usually detected on a large feature map.Considering that most of the detected objects are small-scale targets.Therefore,when the input image size is 640×640 pixels by default,a feature map of size 160×160 pixels is added to the detection layer of the original algorithm,and complete intersection over union(CIoU)is selected as the loss function to achieve more effective detection of helmet wearing and mask wearing.The experimental results show that the mean average precision(mAP-50)of the YOLOv5+network model reaches 93.8%and 92.3%on the helmet-wearing and mask-wearing datasets,respectively,which is both improved compared to the precision of the original algorithm.This method not only meets the speed requirement of real-time detection,but also improves the precision of detection.