Small Object Detection Algorithm for Underwater Organisms based on Improved Transformer

Abstract

In view of the problem of difficult feature extraction and target missed detection, propose an improved transformer based underwater target detection algorithm. The proposed algorithm improves the linear embedding module of the Transformer to supplement the linear embedding information of small objects and builds the local relationship model of each layer feeling field, in order to achieve end-to-end accurate detection. An experiment on URPU underwater target detection data set is conducted. Compared with Swim Transformer, the proposed algorithm detection accuracy is 77.4%, 1. 5%, detection speed is 7 frame/s, 35.6%, the number is 30.4 MB, and compression is 84.1%. Experimental results have shown that the improved algorithm improves the performance of underwater small target detection