Neural Network Associative Pruning for Ship Detection in SAR Images
The all-day and all-weather working characteristics of synthetic aperture radar(SAR)have made it widely used in the fields of marine environment monitoring,marine resources survey and marine disaster prevention and mitigation.Ship target detection based on SAR images is a critical element of SAR image processing,which is of great significance in both military and civilian fields.In this paper,a deep learning based associative pruning method is pro-posed to solve the problem of large parameter computation and high memory consumption of SAR image target detection algorithm.By improving the network,the method prunes the associated convolutions simultaneously,and maps them to the lower dimension after the training to realize the pruning operation.Through experiments on SSDD,SAR-Ship-Data-set and HRSID,it is possible to achieve a pruning rate to more than 70%for FCOS networks under the premise of ensur-ing that the average precision(AP50)decreases by less than 2%,which verifies the effectiveness of the proposed method.
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