Abstract: Abstract—Camouflaged Object Detection (COD) is a formidable computer vision challenge due to the striking resemblance between camouflaged objects and their surroundings. Despite progress in existing methods, they still face significant limitations, particularly in addressing the issues of fuzzy boundaries and the inadequate fusion of local and global features. To address these challenges, we present a multi-scale COD network named Multi-Scale Local-Global Fusion (MSLGF). MSLGF incorporates a Multi-Scale Fusion Module (MSFM), which skillfully integrates feature maps at multiple scales to produce high-fidelity edge features. Additionally, to refine the detection process, a Local-Global Feature Fusion Module (LGFFM) combines local edge details with global semantic information of camouflaged targets, significantly enhancing the accuracy of COD.
