BTDGNet: A Dual-Guided Camouflaged Object Detection Network Leveraging Boundary and Texture Information

Camouflaged object detection aims to identify objects that blend seamlessly with their background, posing a greater challenge compared to general object detection tasks. Due to its ability to recognize camouflaged objects, such detection models hold significant practical value across various fields. To accurately identify camouflaged targets in various complex environments, we designed a dual-guided camouflaged object detection network based on boundary and texture information(BTDGNet). The process consists of two main stages. The first stage is the localization stage, which leverages a convolutional neural network (CNN) to capture boundary and texture information of objects. These features are then fused to achieve coarse localization of the camouflaged objects. In the second stage, the recognition stage, we employ a Transformer to extract global information from the image, enhancing the differentiation between foreground and background. An interactive fusion module is designed to fully exploit and integrate both global and local features, producing precise prediction images. By leveraging boundary and texture information, the model's adaptability to different camouflaged objects is improved. The integration of local and global features enhances the model's detection accuracy from various perspectives, ultimately building a camouflaged object detection model suitable for a wide range of complex scenarios. The proposed method was extensively compared with other state-of-the-art methods across four public datasets, and the results demonstrated superior performance. Furthermore, benefiting from our dual-guidance strategy that leverages both texture and boundary information, our model demonstrates robust performance. We conducted tests on detection tasks across four different domains, and the results confirm that our model can accurately segment camouflaged objects in complex scenes.