Real-time system for automatic cold strip surface defect detection

Abstract

Detection and classification of surface defects of the rolled metal are one of the main tasks for correctly assessing product quality. Historically, these tasks were performed by a human. However, due to a multitude of production factors, such as high rolling rate and temperature of the metal, the results of such human work are rather low. Replacing a human controller with an artificial intelligence system has been relevant for a long time; it is merely necessary within the concept of Industry 4.0. This paper is devoted to the development of the prototype system automatic detection and classification of defects for one of the Iron-and-Steel Works of the Chelyabinsk region in the Russian Federation. The prototype consists of the Preprocessor, Classifier, Server, Database, and User interface. The main focus is on achieving high classification accuracy, which is planned to be obtained through the use of convolutional neural networks.

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

“…Their VSD network achieved an impressive total accuracy of 93.70%. Kostenetskiy et al [198] developed a prototype system for Iron-and-Steel Works in the Russian Federation, leveraging convolutional neural networks (CNNs) to automatically detect and classify defects with a classification accuracy of 98.10% on test data comprising six defect types. Additionally, a defect detection system based on deep learning was proposed in [5], achieving an mAP of 82.30 for detection and a 99.70% accuracy for classification tasks by fusing multilevel features.…”

Surface Defects Classification of Hot Rolled Strip Based on Few-shot Learning

“…Wenyan WANG, 1,2,3) Ziheng WU, 1,2) Kun LU, 1,2) Hongming LONG, 2) Dan LI, 1) Jun ZHANG, 5) Peng CHEN 6) and Bing WANG 1,2,4) * a maximum pooling CNN for surface defects detection of hot rolled strip, and obtained an accuracy of 98.57% with a recognition speed of 0.008s. 6) For the same task, Wang et al developed an improved CNN model with reduced training parameters, and improved the accuracy and speed of defect classification to 99.63% and 333 FPS, respectively.…”

“…Obviously, effective and accurate identification of defects is the key to control product quality. 2,3) In recent years, deep learning-based models for the classification of surface defects of hot rolled strip achieved unprecedented success when a large number of labeled data can be available. [4][5][6][7][8][9][10] For example, on a dataset with 1 800 samples, Li et al proposed a 7-layer convolutional neural network(CNN) for surface defect recognition where an accuracy of 99.05% can be achieved along with a speed of 0.001s for each image detection.…”

Automated Visual Defect Classification for Flat Steel Surface: A Survey

“…Otsu is a classical adaptive threshold method for separating defects from the background in flat steel images [59,69,70], which obtains a threshold value based on the characteristic of the large variance between the background and foreground. Different from the threshold methods, the methods based on edge detection use the first or second derivative to detect edge points by taking advantage of the property of discontinuous pixel values in adjacent regions, such as Robert [71], Sobel [72,73], Prewitt [74], Canny [55] and Kirsch [52,53]. The grayscale of steel strip images is ordinarily nonuniform, the gray value variation cross the background and the defect is sometimes gradual, and the size of defect area is very small, not easy to be recognized by the computer.…”