先验引导的特征金字塔阴影检测网络

阴影检测向来是计算机视觉领域的一个基础性挑战。它需要网络理解图像的全局语义和局部细节信息。本文提出了一种检测阴影区域的先验特征金字塔网络结构。该网络搭建了先验加权模块来提取图像中蕴含的阴影先验信息，通过使用阴影先验信息加权卷积特征，引导网络学习到阴影区域。同时，该网络还应用了特征融合模块来融合粗略的语义信息和自上而下路径中的精细特征，并且加入了后处理，进一步优化网络的预测结果。本文在两个公开的阴影检测基准数据集上进行了实验来评估其网络性能。实验表明，本文的方法能够更准确地检测到阴影，和过去最先进的方法相比也表现出色，在SBU数据集上正确率达到了96.6%，平衡检测错误因子为6.22。 

Prior Feature Pyramid Network for Shadow Detection

Shadow detection is always a basic challenge in the computer vision area. It needs an understanding of global image semantic and local detail information. In this paper, we proposed a novel Prior Feature Pyramid Network for shadow detection. The framework constructed the prior attention module to extract the shadow prior information and employed it to weigh convolutional features to guide the network to learn shadow regions. Meanwhile, we also applied a feature polymerization module to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway and used post-process operation to help the network optimize prediction results. We employed two common shadow detection benchmark datasets and perform experiments to evaluate our network. Experiment results show that our proposed approach can more accurately detect the shadow regions with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach achieves excellent performance with 96.6% accuracy value and 6.22 balance error rate on the SBU dataset.