Underwater Target Detection Based on Improved RT-DETR
Underwater target detection has practical significance in ocean exploration.This study proposes a FERT-DETR network suitable for underwater target detection to address the issues of complex underwater environments and limited target feature extraction due to occlusion and overlap.The proposed model first introduces a feature extraction module,Faster EMA,to replace the BasicBlock of ResNet18 in RT-DETR,which can significantly improve its capability to extract features of underwater targets while effectively reducing the number of parameters and depth of the model.Secondly,a cascaded group attention module,AIFI-CGA,is used in the encoding part to reduce computational redundancy in multi-head attention and improve attention diversity.Finally,a feature pyramid for high-level filtering named HS-FPN is used to replace CCFM,achieving multi-level fusion and improving the accuracy and robustness of detection.The experimental results show that the proposed algorithm,FERT-DETR,improves detection accuracy by 3.1%and 1.7%compared to RT-DETR on the URPC2020 and DUO datasets respectively,compresses the number of parameters by 14.7%,and reduces computational complexity by 9.2%.It can effectively avoid missed and false detection of targets of different sizes in complex underwater environments.
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