Research on few shot target detection method based on decoupling
Aiming at the coupling problem of target detection with few shot intensification,a few shot target detection algorithm based on decoupling is proposed focusing on high-value air targets as the research object.Firstly,a gradient adjustment layer is introduced into the regional candidate network to strengthen the regional candidate network and alleviate the task coupling problem.Secondly,the target detection head is disassembled into two branches,classification and regression,and a parameter-free average attention module is added at the front end to alleviate the feature coupling problem.The proposed algorithm improves the detection performance of few shot target detection and enhances the detection ability of new classes.The experimental results show that the proposed algorithm performs best in the 1,2,3,5 and 10 shot experiments,with average accuracy of 32.5％,35.6％,39.6％,41.2％and 57.4％,respectively.Compared with the two-stage fine-tuning method,the detection performance of the proposed algorithm is greatly improved,which solves the problem of the decline of network detection ability under the coupling contradiction of few shot intensification,and improves the detection accuracy of few shot high-value air targets.
few shot target detectionair targetcoupling issuedeep learning
万方数据
维普
