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

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.