Advanced Battery Defect Detection Using VisionMaster: A Deep Learning Approach to Enhance Accuracy and Efficiency

VisionMaster algorithm development platform is a powerful machine vision software. The platform aims to provide users with efficient and convenient algorithm tools to quickly build visual applications and solve complex visual detection challenges, further promoting the deep application and development of machine vision technology. In this study, the process of battery defect detection utilizing the VisionMaster algorithm platform is thoroughly examined. The detection workflow is initiated by acquiring 2D color images of batteries, followed by the collection of 3D depth maps. These imaging modalities are integrated to provide a comprehensive assessment of battery quality. The VisionMaster platform's embedded vision algorithms are discussed in detail, highlighting their role in the quality evaluation process. The visualization of the scanning process, the interpretation of feedback from the vision system, and the corresponding responses from the lower-level control systems are described.Results from both 2D and 3D camera scans are analyzed to illustrate the advantages and limitations of each method. The study demonstrates the core value of the vision detection system in ensuring quality control in battery manufacturing. The integration of VisionMaster with advanced vision algorithms is shown to significantly enhance the accuracy and reliability of defect detection, thereby improving overall production quality.