# SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection  
[PERSON]  
1 School of Electronic Information, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (X.W.)  
[PERSON]  
1 School of Electronic Information, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (X.W.)  
[PERSON]  
1 School of Electronic Information, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (X.W.)  
[PERSON]  
1 School of Electronic Information, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (X.W.)  
[PERSON]  
2 Department of Public Courses, Wuhan Railway Vocational College of Technology, Wuhan 430205, China; [EMAIL_ADDRESS] 2  
on ultrasound, eddy current, etc. Such methods have the disadvantages of a complicated operation and slow detection speed and are difficult to promote; (3) detection methods based on traditional machine vision (MV). Such methods have made significant progress and yield reliable results in many cases [2], but specific preprocessing approaches are required to extract representative features with expert knowledge [3]. However, the wide variety of rollers and the short cycle of product upgrades all require the feature representation to be redesigned from scratch; (4) the deep-learning-based fault diagnosis method for spectrum signals. As a fast and effective data analysis method, deep learning has been widely used by researchers and has caused breakthroughs in image recognition, computer vision, and other fields [4]. Compared with the traditional MV method, the deep-learning-based method can dig out the deep structure of data and automatically learn the representative features. [PERSON] et al. [5] adopted a method based on deep neural networks (DNN), including the deep Boltzmann machine (DBM), deep belief network (DBN), and stacked autoencoder (SAE), to diagnose and classify the frequency spectrum signals of rollers. [PERSON] et al. [6] constructed their own convolutional neural network (CNN) architecture for spectral signal detection. Similar work also includes models deployed by researchers such as [PERSON] et al. [7], [PERSON] et al. [8], and [PERSON] et al. [9]. Such advanced methods have achieved a high detection accuracy, but they still cannot meet the requirements of some enterprises for visual detection.  
Therefore, using the deep-learning-based method to visually detect the surface defects of rollers is a problem that is unavoidable and worth studying. At present, this method still has many difficulties and challenges. As a data-driven method, the deep learning model often requires massive amounts of data for training to achieve a stable and accurate classification performance. However, in actual production, the appearance of roller defects is very subtle and the probability of occurrence is low, making the number of positive and negative samples extremely unbalanced. Therefore, obtaining a high-performance classifier by training a small amount of data is critical.  
This paper proposes a small data-driven convolutional neural network (SDD-CNN) for roller surface defect inspection and classification. First, a label dilation (LD) method is adopted to solve the problem of imbalance in the number of samples between categories. Second, a semi-supervised data augmentation (SSDA) method is proposed for the problem of insufficient defect samples. This method first trains a coarse network to generate a characteristic response intensity map for each sample. Then, pseudorandom cropping is performed on each sample according to the probability value of the intensity map to achieve more accurate data expansion. Third, four state-of-the-art CNN architectures are used to train the sample images of the roller surface, obtain a high-performance classifier, and finally achieve the visual detection of roller surface defects based on deep learning.