Menet: Camouflaged Object Detection with Boundary Localization in Complex Backgrounds

Abstract

The primary challenge of camouflaged object detection (COD) lies in the high similarity between the target and the complex background, making it difficult for the human eye to distinguish them. Based on the phenomenon that human attention shifts between the target and the background when observing objects, we propose a network model named MENet. This model adopts a three-stage decoupled architecture of "localization-interaction-fusion." In the localization stage, we utilize an attention mechanism-based backbone network (PVT-V2) to generate multi-level features, which can initially locate the target area. In the interaction stage, we design a Contour-Aware Edge Module (CAEM) and an Area Decoder (AD) to capture the target edges and background information respectively, thereby achieving precise localization of the target boundary and reducing interference from background noise. Furthermore, we developed a Boundary Guidance Module (BGM) that effectively injects boundary cues and relevant background information separately into the multi-level features, enhancing the model's ability to detect target edges in complex backgrounds. In the fusion stage, we design two Feature Fusion Modules (FFM and KFFM) to effectively merge multi-level features with precise boundaries and de-noised features, thereby enhancing the prediction performance of camouflaged objects. Extensive experiments on three challenging benchmark datasets demonstrate that our MENet outperforms many existing state-of-the-art methods. Our code is publicly available at: https://github.com/yang19950966666/MENet.git

Keywords: Camouflaged object detectionComplex backgroundEdge informationKolmogorov-Arnold Networks