Research on Defect Detection Method of Motor Control Board Based on Image Processing
Jingde Huang, Zhangyu Huang, Chenyu Li, Jiantong Liu

TL;DR
This paper presents an image processing-based defect detection method for motor control boards, achieving over 99% accuracy and suitable for online industrial quality control.
Contribution
The study develops a specific defect detection model using image processing techniques, including noise suppression and feature extraction, optimized for large-scale production line inspection.
Findings
Detection accuracy exceeds 99%
Effective for large-scale online inspection
Applicable to integrated circuit board defect detection
Abstract
The motor control board has various defects such as inconsistent color differences, incorrect plug-in positions, solder short circuits, and more. These defects directly affect the performance and stability of the motor control board, thereby having a negative impact on product quality. Therefore, studying the defect detection technology of the motor control board is an important means to improve the quality control level of the motor control board. Firstly, the processing methods of digital images about the motor control board were studied, and the noise suppression methods that affect image feature extraction were analyzed. Secondly, a specific model for defect feature extraction and color difference recognition of the tested motor control board was established, and qualified or defective products were determined based on feature thresholds. Thirdly, the search algorithm for defective…
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