WRD-Net: Water Reflection Detection using a parallel attention transformer

In contrast to symmetry detection, Water Reflection Detection (WRD) is less studied. We treat this topic as a Symmetry Axis Point Prediction task which outputs a set of points by implicitly learning Gaussian heat maps and explicitly learning numerical coordinates. We first collect a new data set, namely, the Water Reflection Scene Data Set (WRSD). Then, we introduce a novel Water Reflection Detection Network, i.e., WRD-Net. This network is built on top of a series of Parallel Attention Vision Transformer blocks with the Atrous Spatial Pyramid (ASP-PAViT) that we deliberately design. Each block captures both the local and global features at multiple scales. To our knowledge, neither the WRSD nor the WRD-Net has been used for water reflection detection before. To derive the axis of symmetry, we perform Principal Component Analysis (PCA) on the points predicted. Experimental results show that the WRD-Net outperforms its counterparts and achieves the true positive rate of 0.823 compared with the human annotation. •Collecting and annotating the largest water reflection data set, i.e., Water Reflection Scene Data Set.•Proposing a fully differential Water Reflection Detection Network based on symmetry axis point prediction.•Conducting a series of experiments and providing the community with benchmarks.