Local to global purification strategy to realize collaborative camouflaged object detection

Abstract

The purpose of camouflaged object detection is to detect objects in images that are not easily perceived by human eyes. Aiming at the problems of low recognition performance and unsatisfied texture information extraction in the complex environment in the current camouflaged object detection algorithms, we propose to improve the accuracy by simultaneously detecting a group of images containing the same camouflaged category. Therefore, we put forward a novel method termed local to global purification network (LGPNet) for collaborative camouflaged object detection. Our method comprises two main modules: the Local Detail Mining module (LDM) and the Global Intra-group Feature Extraction module (GIFE). The LDM is designed to exploit diversified detail information via different adaptive kernels and receptive field mechanisms locally, and the GIFE module is invented for feature enhancement and multi-level information aggregation. Specifically, the GIFE first utilizes channel attention and spatial attention mechanisms to enhance high-level semantic information and then aggregates the intra-group characteristics by level. Extensive experiments on CoCOD8K dataset and 4 COD benchmark datasets illustrate the effectiveness and superiority of our method compared to SOTAs.