Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety. Issue 1 (10th December 2021) - Chen, Junjie; Liu, Donghai
Abstract: Foreign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance. HIGHLIGHTS: This study proposes an aerial image processing method to recognize foreign objects (FOs). Simultaneously detect, extract, and classify FO in water channel. Integrate superpixel segmentation and support vector machine for abnormal region detection. Propose a distance-aware algorithm to cluster abnormal regions. Develop a hierarchical voting mechanism for FO classification. Graphical Abstract