An Industrial System for Inspecting Product Quality Based on Machine Vision and Deep Learning

With the breakthrough development of technology in the 4.0 digitalization era, computer vision and deep learning have emerged as promising technologies for industrial quality inspection. By leveraging the power of machine learning algorithms, computer vision systems can automatically detect and classify defects in industrial products with high precision and efficiency. As the system processes more data and identifies more complicated defects, it can become more accurate and efficient in detecting imperfections and ensuring product quality. This paper proposes an inspection system integrated with the YOLOv8 network to assess the quality of products based on their surface. The data multi-threading mechanism is also applied in the system to ensure real-time processing operations. The experimental results show that the proposed system reaches high detection accuracy among different types of defects, at above 90%. Additionally, the proposed model reveals that the scratch defect is the most difficult error to detect, requiring a long time for decision analysis.