多尺度特征融合与交互的伪装目标检测网络

伪装目标检测是一项在复杂场景中定位和识别伪装目标的任务. 目前基于深度神经网络的方法已初步运用, 但在复杂场景下遇到干扰时, 许多方法无法充分利用目标的多级特征来提取丰富的语义信息, 仅依靠固定尺寸特征识别伪装目标. 为解决这一问题, 本文提出了一种基于多尺度特征融合交互的伪装目标检测网络. 该网络包含两个创新设计: 多尺度特征感知模块和双阶段邻级交互模块. 前者旨在通过结合多尺度特征的方式充分捕获复杂场景中丰富的局部-全局场景对比信息. 后者则是整合来自相邻层的特征以利用跨层相关性将有价值的上下文信息从编码器传输到解码器网络. 本文在CHAMELEON、CAMO-Test、COD10K-Test这3个公共数据集上对提出的方法进行了评测并与当前的主流方法对比. 实验结果表明, 本文方法的性能超越了当前的主流方法, 在各项指标上达到了优异的性能水平.

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.