HFENet: Hybrid feature encoder network for detecting salient objects in RGB-thermal images
Fan Sun, Wujie Zhou, Weiqing Yan, Yulai Zhang · Digital Signal Processing · 2024
Deep convolutional neural networks (CNNs) have gained prominence in computer vision applications , including RGB salient object detection (SOD), owing to the advancements in deep learning . Nevertheless, the majority of deep CNNs employ either VGGNet or ResNet as their backbone architecture for extracting image information . This approach may lead to the following problems. 1) Variations between imaging modalities during feature extraction across layers. Cross-modal features across layers are often fused in a single step, resulting in inadequate cross-modal feature extraction. 2) Feature long-range dependence problem in multilayer feature decoding. 3) Image boundary blurring. To address these issues, we initially leverage the advantages offered by the VGGNet and ResNet architectures. Additionally, we present a novel hybrid VGG–ResNet feature encoder for RGB-T SOD. Specifically, we introduce a geometry information aggregation module that effectively combines and enhances the VGGNet spatial features of the RGB-T modalities from the bottom to the top. Moreover, we propose a innovative global saliency perception module that progressively refines the ResNet semantic features from the top to the bottom by integrating both local and global information. Furthermore, we introduce a Pearson-gated module to tackle the challenge of long-range dependence between features. This module utilizes gating to merge features by calculating the Pearson correlation coefficients of the fused features at multiple levels. Lastly, we devise an edge-aware module to precisely learn the contours of salient objects, thereby enhancing the clarity of the object boundaries. Extensive experiments conducted on three RGB-T SOD benchmarks demonstrate that our proposed network surpasses the performance of state-of-the-art methods for SOD.