LDWS-net: A learnable deep wavelet scattering network for RGB salient object detection

Abstract

In the salient object detection task, convolutional neural network (CNN) based models have been extensively used. However, preserving a variety of boundary features of objects is equally important while detecting the salient objects. Detecting salient objects with poor boundaries have a significant detrimental effect on the salient object detection (SOD) models' robustness and accuracy. The proposed method leverages a unique and novel edge-directed salient object detection network, which combines wavelet scattering network features with CNN-based features. This integration enables the network to capture both textural and high-level semantic information, leading to improved SOD performance. Additionally, a learnable wavelet scattering network allows for the efficient collection and preservation of textural aspects of objects. This network is seamlessly embedded into the encoder section of the proposed architecture, enhancing the discriminative power of the model. Furthermore, a weighted feature integration module (WFIM) is proposed in the decoder section to adaptively integrate linked nearby features by evaluating their relevance, resulting in improved representation and discrimination capabilities. Extensive testing on well-known benchmark SOD datasets demonstrates that the proposed LDWS-Net outperforms state-of-the-art techniques, exhibiting accurate identification of salient objects and efficient detection of their edges.