Principle of machine vision defect detection

Machine vision defect detection technology has a wide range of applications in industrial production, medical imaging, security monitoring and other fields, which can improve product quality, production efficiency and safety.

Principle of machine vision defect detection

Machine vision defect detection is the use of computer vision technology to detect and identify product surface defects,

First, the image data of the product surface is collected, usually using a camera or sensor to obtain a high-resolution image. The image is preprocessed, including denoising, contrast enhancement, edge detection and other operations to reduce interference and highlight defect areas. Extract features in the image, such as color, texture, shape, etc., to help distinguish between normal areas and possible defective parts.

By comparing the extracted features to the preset defect criteria, abnormal areas in the image are identified as potential defects. Machine learning algorithms (such as support vector machines, deep learning, etc.) are used to classify and make decisions on detected defect areas to determine whether they are real defects.

Output the test results to the display screen or alarm system to inform the operator to deal with or adjust in time. According to the detection results, the machine learning model is continuously optimized and adjusted to improve the detection accuracy and efficiency.

Deep learning techniques, such as convolutional neural networks, excel in machine vision defect detection, learning complex feature representations and improving detection accuracy.

Image processing techniques include filtering, edge detection, morphological operation, etc., which are used to preprocess images and highlight defects. Key features in images are extracted through feature extraction algorithms (such as HOG, SIFT, etc.) to distinguish between normal and abnormal regions.