多尺度特征多径自适应复用的显著性目标检测.

Due to ignoring the extraction of multi-scale features and the differences between features at different levels, salient object detection still has the problems of incomplete prediction structure and loss of details. To solve these problems, this paper proposes a new saliency detection model M3 Net. The network mainly consists of Multi-scale Features Adaptive Fusion Module (MAF) and Recurrent Feedback Aggregation Module (RFA). MAF can adaptively capture and aggregate multi-scale features at different levels. RFA can effectively prevent feature dilution while aggregating features at different levels in the iterative process. The experimental results on 5 benchmark datasets show that the network outperforms 10 existing networks in three evaluation metrics: Fβ, Em, and MAE. On the DUT-OMRON dataset, the Fβ is 0.4% higher than the second-ranked saliency detection model, and the Em is 0.3% higher. In ECSSD dataset, the Fβ is 0.2% higher than the second-ranked saliency detection model, and the Em is 0.3% higher, and the network also has excellent speed performance [ABSTRACT FROM AUTHOR]