Detecting Defect Copper Parts Based on Machine Vision

The quality detection of copper alloys plays a crucial role in enhancing the factory’s economic and production efficiency, particularly in addressing surface defects and ensuring component size and specification accuracy. This paper proposes a deep learning-based quality detection method for detecting the defect on the surfaces of copper alloy components, encompassing both surface defect detection and external dimensional quality assessment. For defect detection, the method achieves an accuracy of 94% with an average detection time of 29 ms. In dimensional quality detection, the accuracy reaches 96%, with an average detection time of 3 seconds. Validation confirms that this deep learning-based method significantly improves the factory’s detection efficiency.