YO-DETR: A Lightweight End-to-End SAR Ship Detector Using Decoder Head without NMS

Yue Guo, Tianzhu Zhang, Shiqi Chen, Ronghui Zhan, Luzhuo Li, Jun Zhang

Abstract: A lightweight SAR ship detection algorithm is necessary to further meet the demands of military applications. This paper proposes an end-to-end efficient SAR ship target detection algorithm based on RT-DETR called YO-DETR. In the feature extraction network, a CNN-based backbone network is used to replace the transformer-based encoder structure, which retains the original feature extraction capability while reducing the number of parameters. Additionally, in order to retain the characteristic of long-distance feature dependency in transformers, the IRMB module is incorporated into the CNN network to enhance long-distance feature interaction. Finally, the introduction of the decoder head reduces the additional time overhead of traditional NMS during inference. Ultimately, the YO-DETR method achieves a 98.2% mAP on the SSDD dataset with only 5.37M parameters and 10.5M weight size, while the FPS (when batch size is set to 32) also achieves 220.4.