Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

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.

Research on the Water Safety Early Warning for Rural Safe Drinking Water Projects in Qingdao

Water safety early warning is an essential and important basis for sustainable rural safe drinking water projects management and running. in this paper, we firstly analyzed the major influencing factors to water safety early warning. Secondly we establish a water supply safety early-warning assessment index system, and give classification standards for water supply safety early-warning assessment. Further, with the fuzzy assessment theory we propose a fuzzy warning assessment model approach. Finally the model and methods are used in Qingdao, and obtained t the rural water safety early-alarm level is ‘middle warning’.