Design of Industrial Inspection System Based on Machine Vision and Research on Image Processing Algorithm

This study discusses image preprocessing, defect detection algorithm and system design and implementation, emphasizes the importance of preprocessing steps such as denoising, enhancement, restoration and segmentation, and compares traditional image processing methods with methods based on deep learning. A composite image processing algorithm combining convolutional neural network (CNN) and adaptive threshold segmentation is proposed to improve the accuracy and robustness of detection. In the experimental part, precision metal parts and electronic components are selected as the detection objects, and the algorithm is implemented through Python and TensorFlow framework, and tested on a workstation equipped with GPU. The results show that the algorithm performs well in the identification of normal products, but there are some errors in the identification of defective products. The processing time of the system is stable at about 0.05s, which meets the real-time requirements. This study verifies the effectiveness and reliability of the proposed industrial detection system, and provides strong technical support for the automation and intelligence of industrial production lines.