Progressively Aggregating Multi-Scale Scene Context Features for Camouflaged Object Detection

Camouflaged object detection (COD) is a computer vision task that imitates human visual mechanisms to recognize and locate camouflaged objects in complex scenes. However, the current COD methods cannot accurately discriminate the camouflage objects only by the local appearance features of the objects when meeting distractors with similar appearances. To this end, this paper presents a COD network based on progressively aggregating multi-scale scene context features, so that the accurate camouflaged object discrimination is realized by aggregating multi-stage semantic enhanced scene context features. Specifically, the network mainly has two novel designs: U-shape Context-Aware Module(UCAM) and Cross-level Feature Aggregation Module(CFAM). The UCAM aims to sense rich local to global context information such as detailed boundaries, texture features, and color changes of camouflaged objects. The CFAM combines the coordinate direction attention and the multi-level residual progressive feature aggregation mechanism to gradually aggregate complementary features between adjacent levels, strengthen the global semantics of camouflage objects and supplement local details. Extensive evaluations on 4 extremely challenging benchmarks including CHAMELEON, CAMO-Test, COD10K-Test, and NC4K, the experimental results have demonstrated that our model has achieved leading performance compared with state-of-the-art methods. © 2022, Science Press. All right reserved.