# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 2 Related Works  
Detecting defects on steel surfaces is a known problem in literature. After the success of AlexNet in the ImageNet challenge [3], many proposed systems have relied on the use of CNNs for defect detection on such surfaces. While the literature refers to a variety of steel surfaces, their methods were found to be applicable to our problem area of rolling elements. [PERSON] et al. [5] use a CNN with a softmax classification to classify image segments. The segments have a fixed size of \(200\times 200\) px and are classified into one of eight classes. The network is trained on a labeled set of 300 examples per class before augmentation. To collect such a large dataset requires much manual labor and results in costly production downtime. [PERSON] et al. [6] use a cascaded auto-encoder architecture for semantic segmentation. The defect regions found by this part of the classification system are then fed into a traditional CNN with a softmax layer at its end. This CNN classifies the defect into one of three classes. The authors note the systems need for labeled data as one of its main drawbacks. [PERSON] et al. [2] propose a system that to our knowledge allows the finest classification, with 24 classes. The system slices the input image into non-overlapping windows. In our experiments, this can lead to misclassifications when small defects are cut in half by the windowing.  
While all the above papers classify the defect into several given categories, our system only detects the presence or absence of defects. In a multi-class setting, information on the concrete type of defect gives information on possible problems in manufacturing. However, this classification step requires additional training time, labeled data and computational resources. By forgoing a multi-class approach, our system outperforms most existing systems in inference and training time. The system is not required to learn to associate features with specific defect classes. This simplifies the classification problem. If a classification of the found defects into categories is needed later, an additional classification model can be appended to the proposed system. In the resulting two-stage model cascade, this second model would then classify the defects into multiple classes. These classifications would then again allow conclusions to be drawn on problems in the production process. [PERSON] et al. [7] discuss the advantages of those two-stage systems and compare different approaches.  
One of the downsides of supervised classification systems is their need for labeled training data. Outlier detection approaches circumvent this requirement by training on unlabeled data. [PERSON] et al. [8] apply such an outlier detection approach to the problem of detecting defects on steel surfaces.  
The authors of this paper use a CNN to extract features from input images. Outliers are then detected by comparing distances between samples in feature space in a nearest neighbor fashion. While the need for labeled data is a downside of supervised learning approaches, they are shown to perform better than outlier detection approaches in defect detection problems [9; 10].  
Our proposed approach uses simulated defects, avoiding the need for manual labeled data while preserving the advantages of a supervised approach.  
Based on the aforementioned reasons, a CNN was trained for binary image classification. With simulated defects, data requirements in comparison to existing systems were reduced.