关键词: 关键词：隐蔽目标检测, 差异感知, 鸟类目标检测, 特征融合

Abstract: Abstract: This paper proposes a camouflaged small object detection network based on discrepancy sense in substation to address the challenge of detecting camouflaged bird objects in the environment of substations， characterized by small sizes and high similarity with surrounding backgrounds leading to low segmentation accuracy of models. The model leverages a global guidance extraction module to obtain global guidance， preserving original detailed information while enlarging the receptive field. Additionally， a boundary guidance generation module is employed to fuse features from all scales to obtain boundary guidance， thereby mitigating noise interference caused by inter-layer feature interactions. Furthermore， a dual-branch discrepancy perception module is utilized to integrate multiple guidance， progressively refining segmentation results by alternating attention between the target boundary and the surrounding background to amplify their discrepancies layer by layer. Experimental results on a self-built dataset of camouflaged small bird objects around substations demonstrate that the proposed method achieves an improvement of 2.75 percentage points in Intersection over Union compared to other camouflaged object detection algorithms， offering a reliable basis for effectively deterring camouflaged bird objects in substation.

Key words: Key words: camouflaged object detection, discrepancy sense, bird object detection, feature fusion