Camouflaged Object Detection via location-awareness and feature fusion

Camouflaged object detection aims to completely segment objects immersed in their surroundings from the background. However, existing deep learning methods often suffer from the following shortcomings: (1) They have difficulty in accurately perceiving the target location; (2) The extraction of multi-scale feature is insufficient. To address the above problems, we proposed a camouflaged object detection network(LFNet) based on location-awareness and feature fusion. Specifically, we designed a status location module(SLM) that dynamically captures the structural features of targets across spatial and channel dimensions to achieve accurate segmentation. Beyond that, a residual feature fusion module(RFFM) was devised to address the challenge of insufficient multi-scale feature integration. Experiments conducted on three standard datasets(CAMO,COD10K and NC4K) demonstrate that LFNet achieves significant improvements compared with 15 state-of-the-art methods. The code will be available at https://github.com/ZX123445/LFNet.