RF2Net: salient object detection using level set loss and reverse attention fusion feed network

Abstract

Salient object detection is a fundamental problem in the research of image and vision. However, traditional models have low confidence and low recall. Although deep learning methods can better locate objects, the boundaries are often not detailed enough. To address these issues, we propose a salient object detection model (RF2Net) that combines traditional level set methods with deep learning. RF2Net incorporates the idea of level set structured loss and reverse attention mechanism on the basis of F3Net. First, RF2Net uses a new loss function that combines BCE(Binary Cross Entropy) loss, weight level set loss and weight MAE(Mean Absolute Error) loss with multi-indicator joint supervision. Through the role of the level set loss operator, it is possible to better focus on the whole of the image instead of pixel-by-pixel supervision like the BCE loss. The introduction of the reverse attention mechanism can effectively reduce the noise during feature fusion between layers and achieve the purpose of improving accuracy. The experiments are compared with 12 state-of-the-art methods on 4 datasets, and MAE, maxF and avgF are all higher than other algorithms in HKU-IS dataset. At the same time, we also conduct ablation experiments on the DUTS dataset and the ECSSD dataset to verify the effectiveness of the algorithm. The ablation experimental results show that the proposed algorithm can effectively improve the effect of salient object detection.