Camouflaged object detection via local features and selective fusion

Camouflaged Object Detection (COD) aims to accurately identify targets seamlessly integrated into complex surroundings, representing a challenging yet critical visual task. However, existing methods for detecting camouflaged objects perform sub-optimally in scenes with cluttered backgrounds and subtle edge information, primarily due to limitations in capturing local fine-grained features and fusing multi-scale features. To address these challenges, we propose a novel approach, LSFNet, which is designed to enhance the representational capability of the decoder by introducing detail-enhanced feature guidance and integrating an improved feature fusion module, thereby collectively tackling the challenges present in camouflaged object detection tasks. The model comprises two main components: the Local Guidance Augmentation Module (LGAM) effectively supplements high-quality detail information such as boundaries and textures by combining high-level semantic guidance, ensuring accurate identification even in indistinguishable edges. Additionally, a Selective Feature Fusion Perceptor (SFFP) is introduced to filter features extracted by the backbone network, selectively integrate multi-scale contextual information from top to bottom, and effectively suppress noise, achieving refined predictions. Extensive experiments conducted on four benchmark datasets demonstrate that LSFNet significantly outperforms 18 state-of-the-art methods, showcasing its outstanding performance in camouflaged object detection.