Research on Camouflaged Object Segmentation Based on Feature Fusion and Attention Mechanism

Abstract

Camouflaged object detection (COD) aims to detect objects that ‘blend in’ with their surroundings and the lack of a clear boundary between the target object and the background in COD tasks makes accurate detection of targets difficult. Although many innovative algorithms and methods have been developed to improve the results of camouflaged object detection, the problem of poor detection accuracy in complex scenes still exists. To improve the accuracy of camouflage target segmentation, a camouflaged object detection algorithm using contextual feature enhancement and an attention mechanism called amplify and predict network (APNet) is proposed. In this paper, context feature enhancement module (CFEM) and reverse attention prediction module (RAPM) are designed.CFEM can accept multi-level features extracted from the backbone network, and convey the features with enhancement processing to achieve the fusion of multi-level features.RAPM focuses on the edge feature information through the reverse attention mechanism to mine deeper camouflaged target information to achieve and further refine the predicted results. The proposed algorithm achieves weighted F-measure and mean absolute error (MAE) of 0.708 and 0.033 on the COD10K dataset, respectively, and the experimental results on other publicly available datasets are also significantly better than the other 14 state-of-the-art models, and achieves the optimal performance on the four objective evaluation metrics, and the proposed algorithm obtains sharper edge details on COD tasks and improves the prediction performance.