Camouflaged objects detection network via contradiction detection and feature aggregation

Camouflaged Object Detection(COD) aims to segment objects with a similar appearance to the background. There are some problems in existing algorithms, such as blurred edges and incomplete detection. To address the above issues, we propose a novel COD framework termed CFNet. Our network consists of the Contradiction Area Detection Module(CADM) and Feature Aggregation Module(FAM). In the CADM, we propose an improved receptive field mechanism, which utilizes max pooling operation and convolution block to highlight the contradictory areas and refine the edge of the hidden object. Besides, the FAM is designed to connect two adjacent layers via attention and anti-attention strategy, leading to cross-layer feature enhancement and information fusion. More specifically, the self-attention mechanism helps supplement semantic information, and the anti-attention mechanism contributes to removing redundant information. Extensive experiments conducted on the four public COD datasets show the comparable performance of the proposed CFNet with SOTAs, and the ablation experiments demonstrate the effectiveness of the proposed modules.