ME 2 FNet: Muti-level Edge-Enhanced Fusion Network for Camouflaged Object Detection

Camouflaged object detection (COD) is an emerging research direction in computer vision in recent years, aiming to segment objects that are visually integrated with the background, which is a valuable task and has attracted increasing interest from researchers. Since camouflaged objects are integrated with their surroundings, their boundaries are also very blurred, and it becomes an important issue in COD to segment the edges of the objects accurately and completely. To address the above issues, in this article, we propose a novel multi-level edge-enhanced fusion for camouflaged object detection network (ME 2 FNet). Specifically, we design a residual texture enhanced module to obtain more refined features from the noise-filled backbone features. Then, we design an edge extraction module (EEM), which aims to extract effective edge semantic information from low-level features and high-level features by a simple local channel attention mechanism. Finally, we design a boundary-guided fusion module, which aims to fuse the previously obtained prior information. It can fuse the edge information extracted by EEM with the features at different levels of the backbone network, and guide the learning under the supervision of ground truth. At the same time, it fuses the high-level global information with the features at different levels, so that the final predicted edge is clearer and the overall structure is more complete. Extensive experiments on three challenging benchmark datasets have shown that ME 2 FNet outperforms multiple leading-edge models in recent years and achieves advanced results under four widely used evaluation metrics.