摘要
Abstract
Nowadays,synthetic aperture radar(SAR)ship detection based on deep learning has become popular.However,its practical application is limited by a large amount of parameters and high computational memory.Through mimicking teacher model,knowledge distillation is regarded as an effcient model compression method.Whereas,most previous knowledge distillation algorithms are specifically designed for RGB image vision tasks,while their performances are poor when directly applied to ship detecion task of complex SAR image.Through analysis,the above phenomenon is mainly due to the following two drawbacks:(1)the extremely imbalance between areas of foreground and background and(2)lack of distillation on the relation between foreground's and background's pixels.To solve such above two issues,a topol-ogy distance knowledge distillation algorithm based on decoupled features is proposed.Through decoupled distillation,it can alleviate the imbalance problem.Besides,student model can learn the relation between foreground and background from teacher model by topology distance distillation,which improves the robustness against background noise.Compared with previous methods,experimental results show that the proposed distillation method can effectively improve the accuracy of SAR ship detction.For example,Faster R-CNN with ResNet18-C4 backbone has an AP increase of 6.85 percentage points on HRSID dataset,improving from 31.81%to 38.66%.关键词
合成孔径雷达(SAR)图像舰船检测/知识蒸馏/特征解耦Key words
synthetic aperture radar(SAR)image ship detection/knowledge distillation/decoupled feature分类
信息技术与安全科学
