Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry

Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization

Quality Control system for a hot-rolled metal surface

The modern ideas about of quality of products are based on the principle of the absolute satisfaction of requirements of recommendations of the buyer. A presence of surface defects of steel-smelting and rolling origin is peculiar to the production of hot-rolling mill. The automatic surface inspection system (ASIS) includes two digital line video cameras for the filming of the upper and lower surfaces of the flat bar, block of illumination of the upper and lower surfaces of the flat bar, computer equipment. A system that secures 100 % control of the surface of rolled metal (of the upper and lower side) detects automatically and classifies the sheet defects in the real time mode was mounted in the domestic practice in the first time in 2003 on hot rolling mill 2000 JSC «Novolipetsk Iron & Steel Corporation» (NISC). The whole assortment of the mill 2000 was divided for the five groups by the outward appearance of the surface. The works on the identification of defects of hot-rolled metal and widening of data base of knowledge of ASIS were continued after the carrying out of guarantee tests. More than 10 thousand images of defects were added to the data base during the year

The detection of hydrogen induced cracking in welded and seamless steel pipes using acoustic emission and ultrasonic techniques

The detection and location of hydrogen induced cracks In steel pipes is unreliable and time consuming because of the unpredictable nature of the defect and the lack of sensitivity of conventional non destructive testing techniques. Determination of the susceptibility of steels to the formation of hydrogen induced cracking by attack from a sour gas environment has always been based on laboratory testing of small samples where the samples are subjected to attack from all sides. This is unrealistic compared to the in-service situation and a single sided exposure test is more realistic. In this thesis the subject literature is reviewed. Experiments on small samples show that seamless steel is the least susceptible to hydrogen induced cracking, whereas electric welded un directionally formed pipe is the most susceptible. The susceptibility of submerged arc welded pipe depends on the metallurgical form of the pipe but is always less susceptible than the electric welded pipe. Ultrasonic techniques have been used to detect the location of hydrogen induced cracks but manual techniques are labour intensive and unreliable. Four complete pipes were subjected to a sour gas environment from one side, one of which was seamless and one electric welded. These pipes were monitored using a passive non destructive testing technique, acoustic emission. A mechanised ultrasonic scanner was used to examine the last two pipes found to be susceptible to cracking using a specially selected ultrasonic transducer. The acoustic emission data collected was used to detect and locate areas of high acoustic activity produced by the formation of hydrogen Induced cracking. These areas were examined metallographically and shown to include several forms of hydrogen induced cracking. The mechanised ultrasonic technique failed to detect near surface (<1 mm) cracks, but was able to resolve mid wall affected areas. The seamless steel pipe was unaffected, whereas the electric welded pipe was severely affected by the sour gas environment. The automatic welded pipes suffered varying degrees of attack. This supports laboratory based experiments on small samples reported previously by other workers

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation

Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.Comment: 13 pages, 6 figure