Rain Drop Image Data Set for Guo 2018 study - still image set

The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly important contexts of visual surveillance and vehicle autonomy. A key part of this problem is robust raindrop detection such that the potential for performance degradation in effected image regions can be identified. Here we address the problem of raindrop detection in colour video imagery by considering three varying region proposal approaches with secondary classification via a number of novel convolutional neural network architecture variants. This is verified over an extensive dataset with in-frame raindrop annotation to achieve maximal 0.95 detection accuracy with minimal false positives compared to prior work. Our approach is evaluated under a range of environmental conditions typical of all-weather automotive visual sensing applications. | Cited in: n The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks (T. Guo, S. Akcay, P. Adey, T.P. Breckon), In Proc. International Conference on Image Processing, IEEE, pp. 3413-3417, 2018. | This collection contains supporting materials in the form of the image data used in the study.

雨滴诱导的图像畸变会对各类全天候视觉感知应用的性能造成显著负面影响，涵盖视觉监控与车辆自动驾驶这类日益重要的场景。该问题的核心环节之一是实现鲁棒的雨滴检测，从而能够识别受影响图像区域中潜在的性能退化风险。本文针对彩色视频图像中的雨滴检测问题，采用三种不同的区域提议方法，并结合多种新型卷积神经网络（Convolutional Neural Network）架构变体进行二次分类。我们通过包含帧内雨滴标注的大规模数据集验证了所提方法，相较于现有研究，该方法实现了最高0.95的检测精度，且假阳性率极低。我们的方法在全天候汽车视觉感知应用典型的多种环境条件下完成了评估。 | 引用自：《基于卷积神经网络的雨滴检测与分类的区域提议策略差异影响》（T. Guo、S. Akcay、P. Adey、T.P. Breckon），收录于2018年IEEE国际图像处理会议（International Conference on Image Processing）论文集，第3413-3417页。 | 本数据集包含本研究中使用的图像数据形式的辅助材料。