# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
###### Abstract  
The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the aluminum profile surface presents non-uniform and non-periodic features, and this irregular distribution makes it difficult for classical reconstruction networks to accurately reconstruct the normal features, resulting in low performance of related unsupervised detection methods. Aiming at such problems, a feature-oriented reconstruction method of unsupervised surface-defect detection method for aluminum profiles is proposed. The aluminum profile image preprocessing stage uses techniques such as boundary extraction, background removal, and data normalization to process the original image and extract the image of the main part of the aluminum profile, which reduces the influence of irrelevant data features on the algorithm. The essential features learning stage precedes the feature-optimization module to eliminate the texture interference of the irregular distribution of the aluminum profile surface, and image blocks of the area images are reconstructed one by one to extract the features through the mask. The defect-detection stage compares the structural similarity of the feature images before and after the reconstruction, and comprehensively determines the detection results. The experimental results improve detection precision by 1.4% and the \(F1\) value by 1.2% over the existing unsupervised methods, proving the effectiveness and superiority of the proposed method. defect detection; aluminum profile; unsupervised learning; complex texture processing +
Footnote †: journal: Medicaldefects. Although the method has a certain accuracy, it takes a lot of time and manpower and is easily affected by the subjective judgment and fatigue of staff, such that efficiency and reliability are limited. In addition, as the production speed of aluminum profiles increases, manual inspection can no longer meet the production line's demand for inspection speed. Therefore, there is an urgent need to replace inefficient manual work by intelligent means. With the development of computer image-processing technology, the use of machine vision detection methods [1; 2] to determine the surface-defect information in images has emerged. However, surface-defect detection methods based on traditional machine learning models need to extract features manually; have relatively harsh imaging environment requirements; are sensitive to changes in light and viewing angle; find it difficult to detect undefined defects; and are not suitable for complex defect detection. For aluminum profiles with complex surface textures and large differences in defect scales, it is difficult for current models to be effective in practical engineering applications and they cannot meet industrial demands. In recent years, the rise of deep learning techniques has had a wide impact in the field of image processing [3; 4; 5; 6], especially for the tasks of natural image classification, target detection, and localization, which have made significant progress. Compared to traditional machine vision methods, deep learning methods achieve automatic feature extraction through neural networks, which map the original image by layer-by-layer feature transformations, transforming it from the original feature space to a new one, and this method makes target detection easier. However, the convolutional neural network (CNN) method [7; 8] is inefficient in capturing global contextual information and also fails to provide a holistic perception and macroscopic understanding of the image due to the limitation of the sensory field size, which results in limited local information being extracted by the shallow network.