Detection and segmentation method of surgical instruments based on improved YOLOv5s

In the process of endoscopic surgery,surgeons need to know the position information of surgical instruments in real time. The existing target detection algorithms are affected by factors such as reflection and shadow,and there is still optimization space for the accuracy and missed detection rate. This paper proposes a detection and segmentation method of surgical instruments based on improved YOLOv5s. Firstly,the brightness and contrast of images are corrected by Gamma correction algorithm to solve the problems of reflection and shadow occlusion of surgical instruments. Secondly, convolutional block attention module(CBAM) and dynamic convolution module are designed to increase the weight of important feature information,which further improves the accuracy of target detection and reduces the missed detection rate of the model. At the same time,the spatial pyramid pooling module is optimized to expand the receptive field, so as to better identify multi-scale targets. Finally, the feature pyramid networks(FPN)semantic segmentation head is designed to realize the semantic segmentation. Experimental results on endoscopic surgery dataset show that the mAP@ 0. 5 of target detection in this paper is 98. 2%, and the mIoU of semantic segmentation is 94. 0%. The proposed method can assist surgeons to quickly grasp the position and type of surgical instruments,and improve the efficiency of surgery.