A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence

Gao, Yiping; Li, Xinyu; Wang, Xi Vincent; Wang, Lihui; Gao, Liang

doi:10.1016/j.jmsy.2021.05.008

Cited by 112 publications

(36 citation statements)

References 115 publications

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“…Vision-based defect recognition can be loosely divided into designed-feature-based methods and learned-feature-based methods [6]. In particular, the former can be further separated into four types, statistical methods, filter-based methods, structural methods, and model-based methods, according to the defects texture [7].…”

Section: Related Work 21 Methods On Defect Recognitionmentioning

confidence: 99%

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

et al. 2021

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With the advancement of industrial intelligence, defect recognition has become an indispensable part of facilitating surface quality in the steel manufacturing process. To assure product quality, most previous studies were typically trained with many defect samples. Nonetheless, a large quantity of defect samples is difficult to obtain, owing to the rare occurrence of defects. In general, deep learning-based methods underperformed as they have inherent limitations due to inadequate information, thereby restraining the application of models. In this study, a two-level Gaussian pyramid is applied to decompose raw data into different resolution levels simultaneously filtering the noises to acquire compact and representative features. Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when trained using a lightweight dataset. In addition, the experiments conducted using the disturbance defect dataset showed the robustness of the proposed MRFFN against common noises and motion blur.

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Section: Related Work 21 Methods On Defect Recognitionmentioning

confidence: 99%

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

et al. 2021

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“…The survey [73] presents a wide range of different types of models (supervised, semi-supervised, hybrid models) based on different types of application domains (intrusion detection, fraud detection, medical anomaly detection, log anomaly detection, time series anomaly, industrial anomaly, etc.) The paper [74] surveys traditional methods, and also introduces deep learning based methods. For each type of method, the characteristics of each method are listed in a general way.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

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“…Besides, pictures and videos data are collected to initiatively discover the existing quality problems and trace the causes of quality abnormities. On this basis, vision-based recognition methods from a feature perspective are applied to discover the defect [17]. Such methods achieve traceability of quality data through correlation but do not mine the complicated association between multi-source heterogeneous scattered data, so the efficiency of quality problem traceability is low.…”

Section: Quality Data Prediction and Traceability Technologymentioning

confidence: 99%

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

Zhuang

Liu

et al. 2022

Chin. J. Mech. Eng.

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The assembly process of aerospace products such as satellites and rockets has the characteristics of single- or small-batch production, a long development period, high reliability, and frequent disturbances. How to predict and avoid quality abnormalities, quickly locate their causes, and improve product assembly quality and efficiency are urgent engineering issues. As the core technology to realize the integration of virtual and physical space, digital twin (DT) technology can make full use of the low cost, high efficiency, and predictable advantages of digital space to provide a feasible solution to such problems. Hence, a quality management method for the assembly process of aerospace products based on DT is proposed. Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection, the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system. The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace. The implementation of the proposed approach is described, taking the collected centroid data of an aerospace product’s cabin, one of the key quality data in the assembly process of aerospace products, as an example. A DT-based quality management system for the assembly process of aerospace products is developed, which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

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A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence

Cited by 112 publications

References 115 publications

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

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