Floodwater detection on roadways from crowdsourced images
Floodwater detection on roadways from crowdsourced images
复制标题
利用众包图像检测道路洪水
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
K. Iftekharuddin
中科院分区：
文献类型：
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作者：
Megan A. Witherow;Cem Sazara;Irina M. Winter;M. I. Elbakary;M. Cetin;K. Iftekharuddin
摘要
这项工作提出了一种图像处理流程，用于从移动消费设备（如智能手机）捕获和生成的图像数据中检测被淹没道路上的洪水范围。使用从弗吉尼亚州诺福克市实际的滋扰性洪水事件中收集的样本数据集以及位置匹配的参考图像来演示所提出的方法。众包数据的高度可变性表现为位置匹配的干燥/洪水状况图像对之间的差异，这使得提取洪水信息更具挑战性。这些差异可能包括分辨率、光照和环境条件的不同。场景可能包括道路上的动态物体，如车辆和行人。在所提出的流程中，图像经过一系列预处理操作，包括水边缘检测、图像修复和对比度校正。对基于区域的卷积神经网络（R - CNN）进行训练、测试和部署以用于车辆检测。修复过程会去除由R - CNN检测到的车辆。使用尺度不变特征变换流算法对图像进行配准。检测洪水区域的边界。地标和天空/云彩的反射也对淹没区域的检测构成重要挑战。首先识别附近地标的反射，然后通过饱和度通道处理将其用作识别剩余水体（包括天空/云彩的反射）的种子。将结果与检测到的水边缘线进一步处理。去除误报。将所提出的方法应用于现实世界的图像并评估其准确性。结果表明，尽管众包图像数据复杂且环境动态，但该方法仍能产生令人满意的结果。
Abstract This work proposes an image processing pipeline for detecting floodwater extent on inundated roadways from image data captured and generated by mobile consumer devices, such as smartphones. A sample data-set collected from actual nuisance flooding events in Norfolk, VA and location-matched reference images are used to demonstrate the proposed approach. The highly variable nature of crowdsourced data manifests as discrepancies in location-matched dry/flooded condition image pairs, making extracting inundation information more challenging. These discrepancies may include differences in resolution, lighting and environmental conditions. Scenes may include dynamic objects, such as vehicles and pedestrians, on the roadway. In the proposed pipeline, images go through a set of pre-processing operations consisting of water edge detection, image inpainting and contrast correction. A Region-Based Convolutional Neural Network (R-CNN) is trained, tested and deployed for vehicle detection. An inpainting procedure removes vehicles detected by the R-CNN. The images are registered using the Scale Invariant Feature Transform flow algorithm. Boundaries of the flooded area are detected. Reflections of landmarks and sky/clouds also pose an important challenge to detection of inundated areas. Reflections from nearby landmarks are first identified, then used as a seed for identifying the remaining water body, including reflections of sky/clouds, through saturation channel processing. The result is further processed with the detected water edge lines. False positives are removed. The proposed method is applied to real-world images and its accuracy is evaluated. The results show that the method produces satisfactory results despite the complexities of the crowdsourced image data and dynamic environment.