TINYCOD: TINY AND EFFECTIVE MODEL FOR CAMOUFLAGED OBJECT DETECTION

This paper introduces an effective and tiny model for real-time Camouflaged Object Detection (COD) named TinyCOD. It achieves high performance with very low costs (Parameters < 5M, FLOPs< 1.5G), which can be applied on mobile devices. Specifically, we introduce a simple but effective Adjacent Scale Features Fusion module (ASFF), which can significantly enhance the representation ability of features from a lightweight backbone. Besides, as the edge areas of the camouflaged object often blend into the background, we carefully design an Edge Area Focus module (EAF) to solve this problem. Experimental results on COD datasets prove that the proposed method achieves state-of-the-art performance compared with other methods.