Research on Camouflage Target Detection Method Based on Edge Guidance and Multi-Scale Feature Fusion

Camouflaged Object Detection (COD) aims to identify objects that share highly similar patterns—such as texture, intensity, and color—with their surrounding environment. Due to their intrinsic resemblance to the background, camouflaged objects often exhibit vague boundaries and varying scales, making it challenging to accurately locate targets and delineate their indistinct edges. To address this, we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network (EGMFNet), which leverages edge-guided multi-scale integration for enhanced performance. The model incorporates two innovative components: a Multi-scale Fusion Module (MSFM) and an Edge-Guided Attention Module (EGA). These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries. Moreover, recognizing the rich contextual information in fused features, we introduce a Dual-Branch Global Context Module (DGCM) to refine features using extensive global context, thereby generating more informative representations. Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics. Specifically, on COD10K, our EGMFNet-P improves $F_{\beta }$ by 4.8 points and reduces mean absolute error (MAE) by 0.006 compared with ZoomNeXt; on NC4K, it achieves a 3.6-point increase in $F_{\beta }$. On CAMO and CHAMELEON, it obtains 4.5-point increases in $F_{\beta }$, respectively. These consistent gains substantiate the superiority and robustness of EGMFNet.