Camouflaged Object Detection Network Based on Multi-scale Feature Fusion and Interaction
The task of camouflaged object detection involves locating and identifying camouflaged objects in complex scenes.While deep neural network-based methods have been applied to this task,many of them struggle to fully utilize multi-level features of the target for extracting rich semantic information in complex scenes with interference,often relying solely on fixed-size features to identify camouflaged objects.To address this challenge,this study proposes a camouflaged object detection network based on multi-scale and neighbor-level feature fusion.This network comprises two innovative designs:the multi-scale feature perception module and the two-stage neighbor-level interaction module.The former aims to capture rich local-global contrast information in complex scenes by combining multi-scale features.The latter integrates features from adjacent layers to exploit cross-layer correlations and transfer valuable contextual information from the encoder to the decoder network.The proposed method has been evaluated on three public datasets:CHAMELEON,CAMO-Test,and COD10K-Test,and compared with the current mainstream methods.The experimental results demonstrate that the proposed method outperforms the current mainstream methods,achieving excellent performance across all metrics.
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