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.