BFNet: Boundary guidance signal and feature fusion network for camouflaged object detection

Abstract

The purpose of Camouflaged Object Detection (COD) is to identify objects that are visually indistinguishable from their backgrounds due to high similarities in color, texture, and luminance. This task presents greater challenges compared to conventional object detection because of the intricate blending between objects and their surroundings. In this paper, a boundary guidance signal and feature fusion network (BFNet) for camouflaged object detection is proposed. The proposed method mainly consists of three key components: boundary guidance signal module (BGSM), attention-induced feature fusion module (AFFM) and gradual camouflage recognition module (GCRM). BGSM captures edge information to generate edge guidance signals. AFFM fuses cross-level features of shallow and deeper layers to obtain rich details and semantic information. Lastly, the GCRM refines the detection of camouflaged objects step by step to obtain the final prediction map. To verify the effectiveness of the proposed method, relevant experiments were conducted on the four challenging benchmark datasets. The experimental results show that BFNet significantly outperforms the other 16 existing methods under five widely used evaluation metrics.