Scour monitoring is an important measurement process to determine the soil erosion level at the pillar of bridge. Image‐based approach is attractive because it allows monitoring process to be conducted continuously without halting the flow of the water during experiment. Scour images provide abundance of information from a single source of camera sensor. However, this information may appear in different features, orientation, size and brightness. Therefore, it is important to detect and recognise features that are related to scour monitoring and filtered out irrelevant features like image noises and artefacts. This paper presents implementation of image processing techniques to extract various information from scour images. Image inpainting technique is used to separate information of scour level and scale markers into two different images. A proposed gradient of marginal histogram technique is used to detect the horizontal scale line markers and scour level. Backpropagation neural network is used to recognise scale text markers and convert the measurement of the scour level from a pixel unit into a centimetre unit. Interpolation techniques are used to connect scour points and delineate the boundary indicating the level of the scour. Time series acquisition of scour images allows observation of the temporal variation of the scour levels. Results show that the proposed approach achieved higher accuracy than existence method. This approach allows the detection of the scour level for even and uneven sediments, contributing to the high accurate results at the spatial and temporal measurements, thus potentially offering continuous scour monitoring solution.

中文翻译：

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根据时空图像分析测量冲刷水平

冲刷监测是确定桥梁立柱水土流失程度的重要测量过程。基于图像的方法很有吸引力，因为它可以连续进行监视过程，而不会在实验期间停止水流。冲刷图像可从单个摄像机传感器源提供大量信息。但是，此信息可能以不同的功能，方向，大小和亮度出现。因此，重要的是检测和识别与冲刷监控有关的特征，并滤除不相关的特征，例如图像噪声和伪像。本文介绍了从冲刷图像中提取各种信息的图像处理技术的实现。图像修复技术用于将冲刷级别和比例标记的信息分为两个不同的图像。提出的边际直方图梯度技术用于检测水平刻度线标记和冲刷水平。反向传播神经网络用于识别刻度文本标记，并将冲刷级别的测量值从像素单位转换为厘米单位。插值技术用于连接冲刷点并描绘指示冲刷级别的边界。冲刷图像的时间序列采集允许观察冲刷水平的时间变化。结果表明，所提出的方法比已有方法具有更高的准确性。这种方法可以检测均匀和不均匀沉积物的冲刷水平，有助于在空间和时间测量中获得高精度结果，从而有可能提供连续的冲刷监控解决方案。