Two guidance joint network based on coarse map and edge map for camouflaged object detection

Camouflaged object detection (COD) entails identifying objects in an image that blend with the background. However, most traditional COD methods have not comprehensively considered the information provided by the overall region and edges of the objects. To address this problem, a new two-guidance joint network based on coarse and edge maps is proposed for COD. Particularly, an information guidance module is designed to inject edges and overall information into the network's backbone features. Meanwhile, a feature observation model based on skip connections and multi-scale perception is designed to capture multi-scale image details and structures. To avoid the loss of semantic information in low-level features, a full-image attention mechanism is designed to integrate high-level features into low-level features, thereby improving the resolution of the object masks. We compared the proposed network with state-of-the-art models on three well-known datasets, and the experimental results show the proposed network has significant improvement. By exploring valuable boundary information and overall object information, the proposed network can segment object edges while also considering the segmentation effect of the entire object. Our code has been open-sourced at https://github.com/Huah2019/TJNet.