Discriminative Evidence Mining and Reconstruction for Efficient Camouflaged Object Detection

Camouflaged Object Detection (COD) aims to detect objects that closely resemble their background in complex scenes. Existing lightweight COD methods typically reduce computation by compressing feature dimensions and simplifying feature interactions. However, such approaches tend to weaken the subtle discriminative evidence required to distinguish camouflaged objects from their backgrounds, including semantic cues and fine-grained structural details. Therefore, we propose an efficient Discriminative Evidence Mining and Reconstruction Network (DMRNet), which incorporates the Discriminative Evidence Mining (DEM) strategy and the Discriminative Evidence Reconstruction (DER) strategy. Through the DEM, DMRNet captures cross-layer feature discrepancies, with foreground-background competition guiding evidence selection and background interference suppression reducing misleading responses, thereby obtaining more reliable discriminative evidence. Through the DER, DMRNet uses the obtained evidence to guide hierarchical feature reconstruction, thereby enhancing semantic representations at higher levels and fine-grained structural details at the shallow level. DMRNet achieves competitive detection performance on four benchmark datasets while maintaining a low parameter count and computational cost, thereby achieving an effective balance between accuracy and efficiency. The source code will be released upon publication of the paper.