Camouflaged Object Detection via location-awareness and feature fusion

Abstract

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.