The general process of surface scratch detection includes image acquisition, preprocessing, feature extraction and scratch defect identification. Firstly, the image of the object to be detected is obtained by using a suitable light source and camera. Then, the image is preprocessed, including denoising, enhancement and image smoothing to improve the image quality. Next, the image processing algorithm is used to extract the features of the object surface, such as texture, color and shape. Finally, using machine learning or deep learning technology, the model is trained for the extracted features to identify and classify scratch defects.
Surface scratch detection is widely used in many industries, especially in manufacturing and quality control. It can be used to detect scratch defects of automobile parts, electronic products and plastic products, and improve product quality and production efficiency. In addition, surface scratch detection can also be applied to cultural relics protection, medical device detection and other fields.
However, surface scratch detection also faces some challenges. For example, lighting conditions may affect the image quality and the accuracy of scratch detection. Different surface materials and colors have different scratch characteristics, so it is necessary to adjust the algorithm and set the parameters according to different situations.
Generally speaking, surface scratch detection based on machine vision is a challenging and promising technology, which is helpful to improve the efficiency and accuracy of manufacturing and quality control.