# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 2 Related Works  
### Deep Learning Detection Methods  
In surface-defect detection, the earliest manual visual method consumes a lot of manpower and material resources, and it has the problems of low efficiency and poor accuracy. In recent years, machine vision has attracted much attention in surface-defect detection. Some scholars have proposed different methods for object-surface-defect detection using supervised deep learning methods. For example, [PERSON] et al. [9] proposed a non-destructive identification and classification method for surface defects based on the improved YOLOv5 algorithm, and they applied a new mobile network attention mechanism, coordinate attention, to the backbone of the YOLOv5 algorithm, giving better detection results. [PERSON] et al. [10] proposed a cross-layer semantic guidance network (CS-GNet) based on the YOLOv6 algorithm, which introduces a cross-layer semantic guidance module (CSGM) that uses deeper semantic information to guide the shallower feature layer and improves performance for detecting tiny defects. [PERSON] et al. [11] proposed a new method to detect fabric surface defects by target-driven features, Compared with the classical defect algorithm, the algorithm is able to realize accurate segmentation of surface defects, has better noise resistance, higher detection accuracy, and has strong applicability to fabric defect detection. [PERSON] et al. [12] proposed a defect-detection model, McDERT, for aluminum profiles based on the improvement of the classical detector YOLOv4, which is suitable for dealing with the image features of aluminum profiles, and the experimental results show that McDERT is superior to the models such as YOLOv5 and DERT, which effectively improves the defect detection performance. The above methods can more efficiently use the model to automatically extract good features and defective features, and the detection results are more accurate, but the model-training process requires the labeling of a large amount of data and defective samples in advance, and it is very difficult to collect complete defective samples. Several scholars have proposed different methods for object-surface-defect detection using unsupervised methods. For example, [PERSON] et al. [13] proposed a gradient-based unsupervised model, Grad MobileNet, based on MobileNetV3, in which the model the model can be trained using only a few normal images, extracting the feature gradient of the input image, classifying welding defects through the gradient distribution, and achieving 99% accuracy on the welding defect dataset RIAM, which was constructed by the authors. [PERSON] et al. [14] proposed a knowledge-distillation model based on attention mechanism and feature fusion, which enhances the ability of the model to extract features through attention, improves the pixel-level localization of the model, and provides better detection results in the MVTecAD dataset. [PERSON] et al. [15] proposed a fabric-defect-detection method based on an improved generative adversarial network, introducing a center loss constraint to improve the recognition performance of the method, which was evaluated on the publicly available Tianchi dataset with good results. [PERSON] et al. [16] proposed a new unsupervised small-sample-defect-detection model based on the DAGM2007 dataset that performs well with a small number of training samples. Although the above methods are more effective on specific detection targets, their reconstruction networks are difficult to accurately reconstruct irregularly distributed images, and their detection results are susceptible to various factors such as the color, size, and illumination of the detection target.