Image processing-based surface defect detection method

Surface defect detection plays a crucial role in the quality management of precision industrial products and holds significant research value. Defect detection in industrial environments still needs to address two main challenges: first, the proportion of product defects on the surface is too small to capture, presenting a typical small target detection problem; second, some products have complex surfaces with numerous defects, making it difficult to monitor each defect individually. Aiming to address the aforementioned issues, this paper proposes a solution for surface defect detection based on image processing. It successfully detects 11 types of defects on object surfaces and applies this method in industrial settings. First, a lighting platform was built, and defect pictures were collected using industrial cameras and integrated into picture datasets. Secondly, the dataset is preprocessed using image processing to identify four types of defects, and then further processed through image enhancement. The graph is then sent to the deep learning network for training, and the model parameters are adjusted based on the defect state to achieve a convergent model. The experimental results demonstrate that the proposed method can accurately identify 11 types of defects on the product surface with a precision of 98.5%, satisfying the requirements of industrial settings.