Automatic Detection for Battery Leakage Based on DenseNet

Abstract

This article proposes a detection method based on thermal imaging for lead acid-battery leakage. First of all, thermal images were obtained by scanning the lead acid-battery with an infrared camera, and the images were categorized into the two sets of train and test. Then, two methods were introduced to analyze the thermal images to determine whether there was a leakage in the battery. One method used Support Vector Machine (SVM) to train the Local Binary Pattern (LBP) texture features of the images. The other method used deep learning to detect images, and trained the obtained data by DenseNet. The results demonstrate that the two methods are accurate, and show feasibility of thermal imaging to detect lead acid-battery leakage.

Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning

“…Therefore, Xu Jand others can effectively carry out regional leakage control with the help of DMA, which requires the organic cooperation of all departments of the water supply enterprise. If a DMA area has reached the leakage control target after evaluation, it can start to prepare the leakage control report of the area, analyze the investment benefit ratio of leakage control, analyze the problems found in the leakage control process, put forward improvement methods, and guide the main direction of leakage control in the future [7]. Developed countries attach great importance to the research and development of pipe network leakage detection equipment.…”