Attention guided multi-level feature aggregation network for camouflaged object detection

Abstract

Camouflaged object detection (COD) aims to identify objects that are visually blended into their highly similar surroundings, which is an extremely complex and challenging visual task in real-world scenarios, and has recently attracted increasing research interest in the field of computer vision due to its valuable applications. The existing deep learning based methods of COD have the following problems: 1) the ambiguous boundary of the camouflaged objects in prediction map, 2) an inaccurate detection of the camouflaged object with accurate and complete structure details. To this end, an attention guided multi-level feature aggregation network is proposed for this task, which is based on three key designs. First, by embedding spatial pyramid attention (SPA) in ResNet-like backbone network, better multi-level features are extracted to identify the camouflaged objects with complete internal details. Second, an edge context module is designed to make full use of edge information, which highlights camouflaged object structure and generates accurate edge localization of COD. Third, the feature aggregation module based on local attention is used to fuse these enhanced multi-level features and edge context cues, which consists of two major components: the iterative Attentional Feature Fusion (iAFF) module and the Dual-branch Global Context Module (DGCM). Compared with the existing 16 state-of-the-art methods, extensive experiments on four widely-used benchmark datasets under four authoritative evaluation metrics illustrate that the proposed method is very beneficial to the COD task.