关键词: 伪装目标检测, 上下文信息, 区域先验, 多尺度特征

Abstract: Region-prior-based camouflaged (COD) models extract target location information in the early stages and integrate it with visual features to enhance detection accuracy. Current COD algorithms, however, struggle to extract precise target localization under challenging scenarios, resulting in inaccurate regional priors or target omission. The proposed FLMCA-Net addresses these limitations through three dedicated components. Firstly, the positioning module aggregates multi-scale backbone features to capture target positions from global and local perspectives. Secondly, the receptive field enhancement module refines multi-scale feature representations while preserving spatial details. Finally, the context-aware fusion module establishes multi-scale interaction between regional priors and image features, simultaneously extracting global contextual patterns. Experimental results on three publicly available datasets demonstrate that FLMCA-Net outperforms 20 other representative models, with an average performance improvement of 2.5%, 1.8%, and 3.6% over RISNet in terms of , , and , respectively.

Key words: camouflage object detection, contextual information, region prior, multi-scale feature