Twin Network Algorithm for Small Target Detection in Foggy Remote Sensing

doi:10.3778/j.issn.1002-8331.2407-0539

Abstract

Abstract: Aiming at the leakage and misdetection problem of remote sensing small target detection in foggy scenarios, a GFFA-YOLO algorithm based on bipartite network multi-task co-training is proposed. Firstly, the gated fused GFFA network is used to de-fog to recover the target information. Secondly, SD-SCConv and RepNCSPELAN-SD-SCConv modules are designed, which improve the feature extraction capability by fusing the space to the depth layer while using the self-correction mechanism. Finally the selective attention LSK module is added to enhance multi-scale feature fusion. The experimental results show that the proposed algorithm achieves a mAP of 85.6% and 74.3% on the NWPU VHR-10 dataset with different fog concentrations, and 82.1% on the fog-treated DOTA v1.0 dataset, which exhibits a higher detection capability compared to mainstream algorithms.

Key words: YOLO, small target detection, defogging algorithm;attention mechanism

摘要： 针对雾天场景下遥感小目标检测的漏检错检问题，提出了基于双子网多任务协同训练的GFFA-YOLO算法。利用门控融合的GFFA网络去雾来恢复目标信息。设计SD-SCConv和RepNCSPELAN-SD-SCConv模块，该模块通过融合空间到深度层，同时利用自校正机制来提高特征提取能力。增加了选择注意力LSK模块来增强多尺度特征融合。实验结果表明，所提算法在不同雾浓度的NWPU VHR-10数据集上的mAP分别达到85.6%和74.3%，在雾处理后的DOTA v1.0数据集上mAP达到82.1%，相较主流算法表现出更高的检测能力。

关键词: YOLO, 小目标检测, 去雾算法, 注意力机制

SONG Cunli, YANG Jiajun, ZHANG Xuesong. Twin Network Algorithm for Small Target Detection in Foggy Remote Sensing[J]. Computer Engineering and Applications, 2025, 61(9): 128-138.

宋存利, 杨佳俊, 张雪松. 雾天遥感小目标检测的双子网算法[J]. 计算机工程与应用, 2025, 61(9): 128-138.

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