Bmsmlet: boosting multi-scale information on multi-level aggregated features for salient object detection

Nowadays, salient object detection methods based on deep learning have become a research focus. Therefore, how to reveal the representation mechanism and association rules of features at different levels and scales in order to improve the accuracy of salient object detection is a key issue to be solved. This paper proposes a salient object detection method to boost multi-scale information on multi-level aggregated features, which can accurately and flexibly aggregate multi-scale feature information via effective multi-level feature utilization. First, a scalable feature pyramid module is proposed, which can aggregate deep feature information in shallow features, thus obtaining aggregated features between different levels. Then, the global information enhancement module is built in the bottom-up network path to make up for the lost or weakened feature information in the process of multi-scale integration feature transmission. Next, internally convolve each level of aggregated feature via self-interactive module to enrich multi-scale information and improve the multi-scale representation ability of aggregated features. Finally, the global associativity loss function is designed to solve the noise caused by multi-scale variation so as to optimize the network training process, which effectively compensates for the deficiency of cross-entropy in the salient object detection task. The experimental results on four public datasets show that the performance of the proposed method has improved in contrast to the state-of-the-art methods.