Double-Branch Camouflaged Object Detection Method Based on Intra-Layer and Inter-Layer Information Integration

The color and texture of camouflaged objects closely resemble their backgrounds, making it difficult to distinguish between the target and the background. Additionally, camouflaged environments are complex, with some camouflaged objects overlapping significantly with the background, further complicating the identification of camouflaged target areas. To address these challenges, we present a novel double-branch camouflaged object detection algorithm, termed TDNet, inspired by biological vision and based on the classical convolutional neural network ResNet50. TDNet employs a two-stage target detection structure that utilizes location segmentation to accurately identify detection targets. Specifically, we introduce an internal feature interaction mechanism (IFE) that enhances camouflaged target feature representations through multi-dimensional spatial feature enhancement and multi-channel feature interaction, enabling the rough segmentation of camouflaged regions. To mitigate the issue of blurred boundaries of camouflaged targets, we devise a cross-layer feature fusion mechanism (CFU) to refine the borders of camouflaged regions. Moreover, we employ a multi-supervised approach to constrain the model’s degradation process, ensuring the accuracy of camouflaged object detection. Experimental results demonstrate that our proposed method outperforms nine classical camouflaged object detection algorithms, achieving state-of-the-art performance. We will release the code and the test maps at https://github.com/zc199823/TDNet.