# Improved Training of CAE-Based Defect Detectors Using Structural Noise
###### Abstract  
Appearances of products are important to companies as they reflect the quality of their manufacture to customers. Nowadays, visual inspection is conducted by human inspectors. This research attempts to automate this process using Convolutional AutoEncoders (CAE). Our models were trained using images of non-defective parts. Previous research on autoencoders has reported that the accuracy of image regeneration can be improved by adding noise to the training dataset, but no extensive analyse of the noise factor has been done. Therefore, our method compares the effects of two different noise patterns on the models efficiency: Gaussian noise and noise made of a known structure. The test datasets were comprised of \"defective\" parts. Over the experiments, it has mostly been observed that the precision of the CAE sharpened when using noisy data during the training phases. The best results were obtained with structural noise, made of defined shapes randomly corrupting training data. Furthermore, the models were able to process test data that had slightly different positions and rotations compared to the ones found in the training dataset. However, shortcomings appeared when \"regular\" spots (in the training data) and \"defective\" spots (in the test data) partially, or totally, overlapped. artificial intelligence; image processing; feature detection; autoencoder; convolutional autoencoder +
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picture, from a simple sorting of its pixels' values via an intensity histogram to progressive indexes such as texture information or High-order Local AutoCorrelation (HLAC) [3], to mention a few. The typical process, old-fashioned and specific, is to create a reference image that represents the \"average\" appearance of flawless products [4]. This reference is then used to detect the defects on other images, comparing the values of their respective pixels: if targets contain patterns that are not on the \"reference image\", they are considered defective. Even though this technique can be applied to a wide range of products, when no reference is available, generating such an image takes time and requires a lot of samples. Feature extraction is a more modern procedure for analyzing images [5], while being resilient to the variations in the data. Features are sets of data contributing to the categorization of their targets. A few common feature extraction approaches are template matching, deformable template, and graph description. Features extraction algorithms are often used in conjunction with classifiers, so targets can be detected (thanks to the extraction of their features) and categorized (thanks to classifications of the features extracted). The more relevant to the categories they are extracted from, the more useful these features are for classification. There are various classifiers available now such as decision trees [6] and Support Vector Machines (SVMs) [7]. Deep learning and end-to-end solutions have tended to grow in popularity as classifiers because they are able to learn features automatically. In the case of our study, instead of comparing two pictures, one picture is \"scanned\" by several filters to reveal \"features\" that may be categorized as \"flaws\". These filters initially take time to be adjusted, but they virtually work on any image input. When a filter has located and extracted a feature, it is processed by a classifier that labels it as either \"defective\" or \"normal\". If one, or more, features are labeled \"defective\", then the whole part is regarded as \"defective\". Shortcomings remain in both \"traditional\" and \"feature extraction--classification\" methods. In the \"traditional\" method, the detection accuracy heavily relies on the shape, location, and orientation of the targets. Therefore, a complex structure, a slightly unusual outline or even an uncommon position, may lead to poor results.