Lecturer, Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, R.809-No.03 Quang Trung, Da Nang 550000, Vietnam.
Lecturer, International School, Duy Tan University, 254 Nguyen Van Linh, Danang 550000, Vietnam.
Comput Intell Neurosci. 2019 Jul 11;2019:8097213. doi: 10.1155/2019/8097213. eCollection 2019.
To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.
为了保持建筑物的可用性，业主需要了解供水和污水系统的当前状况。因此，及时准确地检测管道表面的腐蚀情况是一项至关重要的任务。传统的人工检测方法由人工检查员执行，耗时耗力。因此，本研究提出了一种基于图像处理的方法，用于实现管道腐蚀检测的自动化。利用图像纹理，包括图像颜色的统计测量、灰度共生矩阵和灰度行程长度，提取管道表面的特征。然后，使用差分花粉授粉优化的支持向量机构建可以识别腐蚀和完好管道表面的决策边界。已经收集并利用一个包含 2000 个图像样本的数据集来训练和测试所提出的混合模型。Wilcoxon 符号秩检验支持的实验结果证实，该方法非常适合感兴趣的任务，准确率为 92.81%。因此，本研究提出的模型可以成为管道系统检测阶段协助建筑维护代理的有前途的工具。
