Recent developments in convolutional neural networks (CNNs) have significantly improved the results of salient object detection (SOD), particularly RGB-D SOD. This article proposes BPA-Net (Boundary-aware Pyramid Attention Network), a network that addresses two key issues in RGB-D SOD based on CNNs: 1) accurately locking the position of an object when it is unclear whether it is a multi-object or a single object, and 2) depicting fine edges and fill pixels while maintaining robustness with complex scenes and similarly-colored backgrounds. Accordingly, we model three network branches to solve these problems separately. To address the first problem, we devise the Multi-scale Attention Branch, a pyramid attention network that collects the positions of objects, thereby eliminating interference from non-objects. The second is addressed via a Boundary Refine Branch that uses a depth image to capture the edges of objects. This step refines the boundaries of objects and emphasizes the importance of salient edge information. Such branches are learned for obtaining precise salient boundaries and for position estimation and are subsequently combined with a coarse salient map generated by the Coarse Salient Detection Branch, an encode-decode SOD network, to improve salient object segmentation. Extensive experiments show that our BPA-Net outperforms state-of-the-art approaches on two popular benchmarks.

卷积神经网络（CNN）的最新发展显着改善了显着目标检测（SOD），尤其是RGB-D SOD的结果。本文提出了BPA-Net（边界感知金字塔注意网络），该网络可解决基于CNN的RGB-D SOD中的两个关键问题：1）在不清楚对象是否为多目标对象时准确锁定其位置对象或单个对象，以及2）描绘精细边缘并填充像素，同时在复杂场景和类似颜色的背景下保持鲁棒性。因此，我们为三个网络分支建模以分别解决这些问题。为了解决第一个问题，我们设计了多尺度注意力分支，这是一个金字塔注意力网络，它收集对象的位置，从而消除了来自非对象的干扰。第二个是通过边界细化分支处理的，该分支使用深度图像捕获对象的边缘。此步骤完善了对象的边界，并强调了显着边缘信息的重要性。学习此类分支以获得精确的显着边界和位置估计，然后将其与由粗略显着检测分支（一种编码解码SOD网络）生成的粗略显着图相结合，以改善显着目标分割。大量的实验表明，我们的BPA-Net在两个流行的基准上均优于最新方法。学习此类分支以获得精确的显着边界和位置估计，然后将其与由粗略显着检测分支（一种编码解码SOD网络）生成的粗略显着图相结合，以改善显着目标分割。大量的实验表明，我们的BPA-Net在两个流行的基准上均优于最新方法。学习此类分支以获得精确的显着边界和位置估计，然后将其与由粗略显着检测分支（一种编码解码SOD网络）生成的粗略显着图相结合，以改善显着目标分割。大量的实验表明，我们的BPA-Net在两个流行的基准上均优于最新方法。