Medical waste detection base on improved YOLOv5-s

Aiming at the characteristics of high detection accuracy and fast detection speed in the task of medical waste detection, this paper proposes an improved YOLOv5-s object detection model. Firstly, the Convolutional Block Attention Module (CBAM) is added to the original YOLOv5-s model backbone network to enhance the attention of the network model to medical waste; then the CIoU loss is used to replace the IoU loss to enhance the positioning accuracy and accelerate the convergence speed of the network model. This paper uses a variety of data enhancement methods to expand the experimental data set, a large number of experiments show that the mean average precision of this improved model reaches 90.73%, compared to the original YOLOv5-s model improved by 2.66%, detection speed reaches 115.7frame/s, the detection effect is better than the current mainstream object detection model.