Detection of surgical instruments based on Gaussian kernel

Abstract

In minimally invasive laparoscopic surgery, it is of practical significance to quickly locate the location and category information of the surgical instrument. It can remind medical personnel of irreversible injury caused to patients due to leaving surgical instruments after the operation. In this paper, the Gaussian kernel is introduced into each ground truth, which is conducive to making full use of label information to allocate positive and negative samples and improve the accuracy of location and classification. Then, we introduce SIoU Loss and Harmonic Loss function into a total loss. The former uses relative coordinates to make the network converge more quickly, and the latter solves the problem of asynchronous optimization of the two branches of classification and regression. Our experiment proves that the strategy based on Gaussian kernel sample allocation is very effective on a pubic data set m2cai16-tool-locations, displaying our method possesses conspicuous accuracy of classification and regression than other work.