# Surgical Instrument Recognition Based on Improved YOLOv5  
[PERSON]  
1 College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China; [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (H.W.) [PERSON]  
2 College of Engineering, Huaqiao University, Quanzhou 362021, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
1 College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China; [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (H.W.) [PERSON]  
2 College of Engineering, Huaqiao University, Quanzhou 362021, China; [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
2 College of Engineering, Huaqiao University, Quanzhou 362021, China; [EMAIL_ADDRESS] (L.Y. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.L. ); 23014084008@stu.hqu. the Single Shot MultiBox Detector (SSD) in 2015. It is designed to detect multiple object categories in a single forward pass through the network, making it suitable for real-time applications. In addition, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of different sizes [6; 7]. The original FPN architecture was introduced by [PERSON] et al. in 2016. FPNs combine features from different layers of a convolutional network to improve detection performance [8; 9]. The FPN architecture is independent of the backbone convolutional architectures, making it a generic solution for building feature pyramids within deep convolutional networks. Deep learning has also made remarkable progress in the field of surgical instrument detection. [PERSON] et al. investigated the segmentation and detection of surgical instruments in laparoscopic gynecology videos based on Mask R-CNN [10]. [PERSON] et al. proposed a multi-label classification method to detect the presence of surgical instruments in laparoscopic videos. They experimented with two methods, one using VGGNet and the other using GoogleNet. Then, they combined both models, and the ensemble results produced better results than the single model [11]. [PERSON] et al. used a convolutional neural network based on You Only Look Once version 2 (YOLOv2) as the network model for training and nine video recordings of carotid endarterectomies for training and testing [12]. [PERSON] et al. introduced and investigated a method for simultaneous detection and processing of micro-instruments and gaze during microsurgery [13]. They trained and evaluated a fast CNN-based detector to perform tool detection from a dataset of approximately 20 surgical positions and 17 micro-instruments. However, in the past, the application of deep learning in the medical field usually focused on the detection of the position of surgical instruments during surgery to enable machine control of surgical instruments. There has been less relevant application for the detection of surgical instruments before and after use. In view of the various defects of manual detection of surgical instruments, this article proposes the automation of surgical instrument detection, combines it with the current image recognition field of deep learning, and improves the YOLOv5 to make it suitable for surgical instrument recognition. The main works of this paper are as follows: (a) We establish a standard surgical instrument dataset based on eight types of representative surgical instruments and extended by a random-mixed image augmentation method. (b) We optimize the backbone network combined with the attention mechanism to make it more suitable for the detection of surgical instruments. (c) We use the dynamic sparse convolution algorithm instead of ordinary convolution to reduce the computational complexity. (d) We optimize the loss function and replace the original IoU metric with Wise-IoU.