计算机科学与探索2025，Vol.19Issue(4)：1001-1010,10.DOI:10.3778/j.issn.1673-9418.2403058
边缘-分割交叉引导的伪装目标检测网络
Edge-Segmentation Cross-Guided Camouflage Object Detection Network
摘要
Abstract
The camouflage object detection based on edge-aware model is one of the mainstream methods,and its core is to output edge prediction at an early stage,which can better locate and segment camouflage objects.However,in the camou-flage object dataset,due to the high visual similarity between the camouflage object and the background environment,the quality of early edge prediction is very high,and the incorrect foreground prediction will lead to incomplete segmentation or even missing objects,resulting in poor camouflage object segmentation.To address this issue,an edge-segmentation cross-guided camouflage object detection network(ECGNet)is proposed.Firstly,the ConvNeXt model is used as the backbone network,and the feature channels are processed uniformly through 1×1 convolution,and the global context information is extracted at multiple scales.Secondly,a segmentation-induced edge fusion module and an edge-perception guided integrity aggregation module are designed to cross-fuse,focusing on the overall structure of the camouflage object,and continuously refining the segmentation features and edge features.Finally,by guiding the residual channel attention module,these connections and convolutions are used to better extract structural details from low-level features.Experimental results on the datasets CAMO,COD10K and NC4K show that ECGNet outperforms the other 22 representative models,and compared with HitNet,the performance of Sα,Eϕ,Fωβ and M is improved by 0.019,0.019,0.018 and 0.009 on average.关键词
伪装目标检测/上下文信息/交叉细化/边缘感知Key words
camouflage object detection/contextual information/cross-refinement/edge-aware分类
信息技术与安全科学引用本文复制引用
陈鹏,李旭,向道岸,余肖生..边缘-分割交叉引导的伪装目标检测网络[J].计算机科学与探索,2025,19(4):1001-1010,10.基金项目
国家重点研发计划(2016YFC0802500).This work was supported by the National Key Research and Development Program of China(2016YFC0802500). （2016YFC0802500）
