ECNet: An edge-guided and cross-image perception network for collaborative camouflaged object detection

Traditional camouflaged object detection (COD) methods typically focus on individual images, ignoring the contextual information from multiple related images. However, objects are often captured in multiple images or from different viewpoints in real scenarios. Leveraging collaborative information from multiple images can achieve more robust and accurate detection. This collaborative approach, known as “Collaborative Camouflaged Object Detection (CoCOD)”, addresses the limitations of single-image methods by exploiting complementary information from multiple images, enhancing detection performance. Recent advancements in CoCOD have shown notable progress. However, challenges remain in effectively extracting multi-scale features and facilitating cross-attention feature interactions. To address these limitations, we propose a novel framework, named the Edge-Guided and Cross-Image Perception Network (ECNet). The ECNet consists of two core components: the edge-guided scale module (EGSM) and the cross-image perception enhancement module (CPEM). Specifically, EGSM enhances feature extraction by integrating edge-aware guidance with multi-scale asymmetric convolutions. Meanwhile, CPEM strengthens cross-image feature interaction by introducing collaborative attention, which reinforces semantic consistency among correlated targets and suppresses distracting background information. By integrating edge-aware features across multiple spatial scales and cross-image semantic consistency, ECNet effectively addresses the challenges of camouflage detection in visually complex scenarios. Extensive experiments on the CoCOD8K dataset demonstrate that our proposed ECNet outperforms 18 state-of-the-art COD methods, 11 co-salient object detection (CoSOD) models, and 4 CoCOD approaches, as evaluated by six widely used metrics.