Edge-Segmentation Cross-Guided Camouflage Object Detection Network

doi:10.3778/j.issn.1673-9418.2403058

Abstract

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 camouflage 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],[E?],[Fωβ] and [M] is improved by 0.019, 0.019, 0.018 and 0.009 on average.

Key words: camouflage object detection, contextual information, cross-refinement, edge-aware

摘要： 基于边缘感知的模型是伪装目标检测的主流方法之一，其核心是在早期阶段输出边缘预测，能更好地定位和分割伪装目标。而在伪装目标数据集中，由于伪装目标与背景环境有很高的视觉相似性，对早期的边缘预测质量要求很高，错误的前景预测会导致分割不完整，甚至缺失目标，进而造成伪装目标分割效果不佳。为了解决这一问题，提出了一种边缘-分割交叉引导网络ECGNet。利用ConvNeXt模型作为骨干网络，通过1×1卷积对特征通道进行统一处理，在多尺度上提取全局上下文信息。设计了一个分割诱导边缘融合模块和一个边缘感知引导完整性聚合模块交叉融合，关注伪装目标的整体结构，不断细化分割特征和边缘特征。通过引导残差通道注意模块利用这些连接和卷积更好地提取低层特征中的结构细节。在CAMO、COD10K以及NC4K数据集上的实验结果表明，ECGNet性能优于其他22个具有代表性的模型，比HitNet在[Sα]、[E?]、[Fωβ]和[M]方面的性能平均提升了0.019、0.019、0.018和0.009。

关键词: 伪装目标检测, 上下文信息, 交叉细化, 边缘感知

CHEN Peng, LI Xu, XIANG Dao’an, YU Xiaosheng. Edge-Segmentation Cross-Guided Camouflage Object Detection Network[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(4): 1001-1010.

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.

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