Fraud detection in water meters using pattern recognition techniques

Water supply utilities have been increasingly looking for solutions to reduce water wastage. Many efforts have been made aiming to promote a better management of this resource. Fraud detection is one of these actions, as the irregular violations are usually held precariously, thus, causing leaks. In this context, the use of technology in order to automate the identification of potential frauds can be an important support tool to avoid water waste. Thus, this research aims to apply pattern recognition techniques in the implementation of an automated detection of suspected irregularities cases in water meters, through image analysis. The proposed computer vision system is composed of three steps: the detection of the water meter location, obtained by OPF classifier and HOG descriptor, detecting the seals through morphological image processing and segmentation methods; and the classification of frauds, in which the condition of the water meter seals is assessed. We validated the proposed framework using a dataset containing images of water meter inspections. At the last step, the proposed framework reached an average accuracy up to 81.29%. We concluded that a computer vision system is a promising strategy and has potential to benefit the analysis of fraud detection.