# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 1 Introduction  
Hot-rolled steel plates are widely used in engineering fields such as ship, bridge, machinery, construction, and automobile manufacturing [1]. In the production process of a plate, due to the rolling process, various types of defects can easily form on the surface of the hot-rolled steel plates, such as warping, edge cracking, edge defects, crusting, oxidation, holes, and roll marks. These defects have a great impact on the appearance and performance of the product, so it is extremely important to detect the surface defects on the plate [2].  
At present, domestically, surface defect detection is performed mainly by traditional machine learning methods [3; 4] and deep learning methods based on convolutional neural network (CNN) networks [5; 6]. This method has a high detection rate for non-periodic defects. For example, [PERSON] et al. [7] proposed an end-to-end defect detection system for steel strips [7]. In this system, the deep convolutional neural network (CNN) is adopted, the defect image is directly taken as the input, and the defect category is taken as the output. The experiment shows that the detection rate of this method for non-periodic surface defects is above 96%. [PERSON] et al. [8] proposed a new target detection framework called category priority network (CPN) and multi-group convolutional neural network (MG-CNN). In CPN, the image is first classified by MG-CNN. Then, according to the classification result, the feature map group that may contain the defect is separately inputted into another neural network based on yolo (you only look once: a real-time object detection system) [9] for defect location. The average detection rate of this method for non-periodic surface defects is above 96%.  
However, the morphological features of periodic defects, such as roll marks, are not fixed. The traditional CNN classifies by extracting morphological features, and the defects with unfixed morphological features are easily misclassified, so that the detection rate is not high.  
As periodic defects, such as roll marks, have strong time-sequenced characteristics, long short-term memory (LSTM) [10; 11; 12] was introduced, and a periodic defect detection method based on CNN + LSTM was developed. This method combines the advantages of CNN and LSTM. The feature vectors of defect images are first extracted through the CNN, and the extracted feature vectors are inputted into the LSTM for defect recognition.  
In view of the non-accurate use of previous effective information of CNN + LSTM, the attention mechanism [13] with the Encoder-decoder framework [14] was introduced to improve the method. This improved method specifies the importance of input information at each previous moment, and gives the quantitative weight according to the importance.  
This article has five sections. Section 1 introduces periodic defects and explains the importance of detecting them. Section 2 introduces the features of periodic defects and amplifies the samples to provide data support for subsequent algorithm research. Section 3 introduces the periodic defect detection algorithm based on CNN + LSTM, and then uses the amplified samples to carry-out model training and testing, and analyzes the results. Section 4 introduces the improved detection algorithm with an attention mechanism for the problems in the experiment of Section 3, and experiments using the improved algorithm to verify its effectiveness. Section 5 summarizes the advantages and disadvantages of the algorithm introduced in this paper, and looks forward to the direction of its improvement in the future.