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Received:May 08, 2024 Revised:May 29, 2024
Received:May 08, 2024 Revised:May 29, 2024
中文摘要: 水下目标检测技术在海洋探测中具有重要的现实意义. 针对水下场景复杂, 以及存在遮挡重叠导致目标特征提取有限的问题, 提出了一种适用于水下目标检测的FERT-DETR网络. 该模型首先提出了一种特征提取模块Faster-EMA, 用于替换RT-DETR中ResNet18的BasicBlock, 能够在有效降低模型的参数量和模型深度的同时, 显著提升对水下目标的特征提取能力; 其次在编码部分使用级联群体注意力模块AIFI-CGA, 减少多头注意力中的计算冗余, 提高注意力的多样性; 最后使用高水平筛选特征金字塔HS-FPN替换CCFM, 实现多层次融合, 提高检测的准确性和鲁棒性. 实验结果表明, 所提算法FERT-DETR在URPC2020数据集和DUO数据集上较RT-DETR检测准确率提高了3.1%和1.7%, 参数量压缩了14.7%, 计算量减少了9.2%, 能够有效改善水下复杂环境中不同尺寸目标漏检、误检的问题.
中文关键词: 计算机视觉 RT-DETR FasterNet 注意力机制 高水平筛选特征金字塔
Abstract: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.
keywords: computer vision RT-DETR FasterNet attention mechanism high level filtering feature pyramid
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基金项目:湖北省教育厅项目(B2019077)
引用文本:
张路,魏本昌,魏鸿奥,周龙刚.基于改进RT-DETR的水下目标检测.计算机系统应用,,():1-10
ZHANG Lu,WEI Ben-Chang,WEI Hong-Ao,ZHOU Long-Gang.Underwater Target Detection Based on Improved RT-DETR.COMPUTER SYSTEMS APPLICATIONS,,():1-10
张路,魏本昌,魏鸿奥,周龙刚.基于改进RT-DETR的水下目标检测.计算机系统应用,,():1-10
ZHANG Lu,WEI Ben-Chang,WEI Hong-Ao,ZHOU Long-Gang.Underwater Target Detection Based on Improved RT-DETR.COMPUTER SYSTEMS APPLICATIONS,,():1-10