# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 1 Introduction  
Quality inspection and control in the steel-manufacturing industry have been a critical issue for assuring the product quality and increasing its productivity. As a steel defect is deemed to be one of the main causes of the production cost increase, monitoring the quality of steel products is inevitable during the manufacturing process [1]. The defects can be attributed to various factors, e.g., operational conditions and facilities [2; 3]. For an immediate response and control about the flaws, detecting steel defects should be preceded to analyze the failure causes. To this end, a sophisticated diagnostic model is required to detect the failures properly and to enhance the capability of quality control. In particular, a vision-based diagnostics system for detecting the steel surface defects has received considerable attention. The traditional human inspection system has several disadvantages such as a less-automatic and time-consuming procedure [4; 5]. An image-based system, on the other hand, is developed to enable more elaborate, rapid and automatic inspection than the existing methods [6]. Furthermore, it is widely known that the surface defect accounts for more than 90% of entire defects in steel products, e.g., plate and strip [7]. Defects on the steel surface, e.g., scratches, patches, and inclusions exert maleficent influence on material properties, i.e., fatigue strength and corrosion resistance, as well as the appearance [8]. Likewise, the development of a visual inspection system for identifying steel surface defects should be conducted to secure the reliability of the process and the product. Over recent years, a variety of research-based on machine learning and deep learning techniques have been conducted to establish defect diagnostics model of the steel surface with machine-vision, showing feasible performance for an automatic inspection system. For example, [PERSON], et al. [9] suggest a real-time surface defect detection method using a support vector machine (SVM) classifier, demonstrating the prediction accuracy of 85%. [PERSON], et al. [10] present feature extraction and region-based defect detection method via scale-invariant feature transform (SIFT) to enhance the accuracy with limited samples. Especially, convolutional neural network (CNN) based detection methods are widely utilized as a way of the end-to-end framework for image processing, feature learning, and classification, achieving remarkable improvement in diagnosis performance [11; 12; 13]. To take advantage of the deep learning framework, many researches have been conducted, in particular, application with CNN. A max-pooling CNN for steel defect classification is introduced in [14]. A max-pooling CNN approach is used for classifying seven different types of steel defects from a real production line. The proposed network uses supervised feature extraction directly from the steel images so that it enables to work without prior knowledge. The optimal model of the proposed one attains an error rate of 7%. Work in [15] considers an image-based method for detecting corner cracks of steel billet surface using wavelet reconstruction to reduce the influence of scales. However, its processing time requires a more considerable cost than a deep neural network-based method due to the computation of the wavelet decomposition and reconstruction. An ensemble of support vector machine (SVM) classifiers for steel surface defects with a small sample set is introduced in [16]. Some features are obtained from a set of extractors techniques for being used as the inputs of an SVM ensemble, where a combiner, called Bayes kernel is employed to fuse the results from SVM classifiers. [PERSON], et al. [7] suggest an integrated framework with CNN and naive Bayes data fusion scheme for detecting cracks in nuclear power plants.