基于卷积神经网络的下水道视频障碍物检测
Obstruction level detection of sewer videos using convolutional neural networks
摘要：
在世界范围内，下水道网络的设计是将废水输送到集中处理厂进行处理并返回环境。这一过程对当前社会至关重要，可以预防水传播疾病，提供安全饮用水，并改善一般卫生条件。为了保持下水道网络的完美运行，经常进行抽样检查，以确定障碍物。通常，闭路电视系统用于记录管道内部并报告阻塞程度，这可能会触发清洁操作。目前，障碍水平评估是手动完成的，这是耗时且不一致的。在这项工作中，我们设计了一种方法来训练一个卷积神经网络，用于识别管道中的障碍物的水平，从而减少了这样一个频繁和重复的任务所需的人力。我们收集了一个视频数据库，对这些视频进行了探索和调整，以生成有用的帧，并将其输入模型。我们得到的分类器获得部署就绪的性能。为了验证该方法的一致性及其工业适用性，我们集成了分层相关传播可解释性技术，这使我们能够进一步了解神经网络的行为。最后，该系统可以提供更高的速度，准确性和一致性的过程中，下水道检查。我们的分析还揭示了如何进一步提高数据收集方法质量的一些指导方针。
英文摘要：
Worldwide, sewer networks are designed to transport wastewater to a centralized treatment plant to be treated and returned to the environment. This process is critical for the current society, preventing waterborne illnesses, providing safe drinking water and enhancing general sanitation. To keep a sewer network perfectly operational, sampling inspections are performed constantly to identify obstructions. Typically, a Closed-Circuit Television system is used to record the inside of pipes and report the obstruction level, which may trigger a cleaning operative. Currently, the obstruction level assessment is done manually, which is time-consuming and inconsistent. In this work, we design a methodology to train a Convolutional Neural Network for identifying the level of obstruction in pipes, thus reducing the human effort required on such a frequent and repetitive task. We gathered a database of videos that are explored and adapted to generate useful frames to fed into the model. Our resulting classifier obtains deployment ready performances. To validate the consistency of the approach and its industrial applicability, we integrate the Layer-wise Relevance Propagation explainability technique, which enables us to further understand the behavior of the neural network for this task. In the end, the proposed system can provide higher speed, accuracy, and consistency in the process of sewer examination. Our analysis also uncovers some guidelines on how to further improve the quality of the data gathering methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
