BDFGNet: A Lightweight Salient Object Detection Network Based on Background Denoising and Feature Generation

Since its inception, salient object detection has been widely used in various image processing tasks. However, previous approaches have mainly focused on how to optimize the accuracy of detection, ignoring the computational overhead of the model itself. This makes it difficult to be used in many devices with limited hardware conditions. To address the above problems, this paper proposes a lightweight saliency detection network based on background denoising and feature generation. First, we build a lightweight feature extraction network by improving the MobileNetV3 feature extraction part and with the help of a multidimensional focus module. Subsequently, in order to fully explore the difference between the background and the salient objects. We insert a noise suppression subnet at the end of the encoder. This subnetwork uses the learned background features to reverse fuse the information from the backbone network, effectively decreasing the amount of background noise and encouraging the model to better distinguish subtle differences between salient objects and the background. Finally, in the decoding stage this paper constructs a feature generator that allows the model to efficiently extend the feature map with little increase in the number of parameters. Thereby, it compensates for the lack of expression capability of lightweight model features. The experiment results on six publicly available benchmark datasets prove that our method is more efficient and accurate than existing lightweight saliency detection methods.