Edge-texture Guided Enhancement Network for Camouflaged Object Detection

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Edge-texture Guided Enhancement Network for Camouflaged Object Detection

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[1]WEI Mingjun,CHEN Xiaoru,LIU Ming,et al.Edge-texture Guided Enhancement Network for Camouflaged Object Detection[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):9-17.[doi:10.13705/j.issn.1671-6833.2025.02.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 9-17 Column: Public date: 2025-08-10

Title:: Edge-texture Guided Enhancement Network for Camouflaged Object Detection

Author(s):: WEI Mingjun¹; 2; CHEN Xiaoru¹; LIU Ming¹; LIU Yazhi¹; 2; LI Hui¹; 1.College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China; 2.Hebei Provincial Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, China

Keywords:: camouflaged object detection; edge information; texture information; feature guide; feature interaction

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.02.009

Abstract:: Camouflaged object detection (COD) is facing significant challenges due to the high similarity between target objects and their background, such as blurred edge predictions, incomplete detection results, and interference caused by the insufficient use of edge and texture information. To address the issues of current COD, a novel edge-texture guided enhancement network (ETGENet) was proposed to further improve the performance of COD through explicit and sufficient edge-texture guidance strategies. Firstly, a key feature guided enhancement module (FGEM) was used in ETGENet, which could use parallel feature refinement branches to process and enhance object features. The guide branch could obtain object features by guiding attention correlation with edge and texture cues to enhance the network′s understanding of object details and suppress noise interference. While the self-enhancement branch could use the self-attention mechanism to refine the characteristics of camouflaged objects from a global perspective. Secondly, a feature interaction fusion module (FIFM) was also proposed to progressively fuse adjacent features. FIFM could utilize the attention interaction mechanism and weighted fusion strategy to learn complementary information between features to generate more complete predicted map. Experiments on three public datasets CAMO, COD10K, and NC4K demonstrate that the proposed network outperformed state-of-the-art methods in the field across metrics such as structure measure S, adaptive enhanced matching measure E, weighted F-measure, and mean absolute error M. Notably, on the largest test set, NC4K, the weighted F-measure surpassed the best-performing method among the 12 advanced COD methods, FSPNet, by 2.2 percentage points.

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Last Update: 2025-09-19