An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces

Zhang, Jiawei; Wang, Heying; Tian, Ying; Liu, Kun

doi:10.1016/j.compind.2020.103231

Cited by 29 publications

(16 citation statements)

References 35 publications

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“…Authors Zhang et al [ 3 ] proposed a new, fuzzy defect detection method aimed at sets. Unlike the traditional methods of defect detection, this method uses a membership function to gauge if any particular grey level may be a suspected defect.…”

Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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“…Several techniques have been suggested for identifying defects in various materials and surfaces using statistical methods. For example, the method described in 20 employed fuzzy connected components to identify defects present on strip steel surfaces by calculating the maximum and sum of fuzzy connected areas, resulting in a detection rate of 96.8%. In, 10 an artificial vision-based system for evaluating the quality of slate slabs is presented, using 3D and 2D color data that is processed and analyzed to detect six specific traits.…”

Section: Traditional Methodsmentioning

confidence: 99%

Convolutional neural networks for real-time wood plank detection and defect segmentation

et al. 2023

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Background: Defect detection and segmentation on product surfaces in industry has become one of the most important steps in quality control. There are many sophisticated hardware and software tools used in the industry for this purpose. The need for the real-time classification and detection of defects in industrial quality control has become a crucial requirement. Most algorithms and deep neural network architectures require expensive hardware to perform inference in real-time. This necessitates the design of architectures that are light-weight and suitable for deployment in industrial environments. Methods: In this study, we introduce a novel method for detecting wood planks on a fast-moving conveyor and using a convolutional neural network (CNN) to segment surface defects in real-time. A backbone network is trained with a large-scale image dataset. A dataset of 5000 images is created with proper annotation of wood planks and defects. In addition, a data augmentation technique is employed to enhance the accuracy of the model. Furthermore, we examine both statistical and deep learning-based approaches to identify and separate defects using the latest methods. Results: Our plank detection method achieved an impressive mean average precision of 97% and 96% of global pixel accuracy for defect segmentation. This remarkable performance is made possible by the real-time processing capabilities of our system, which can run at 30 frames per second (FPS) without sacrificing accuracy. Conclusions: The results of our study demonstrate the potential of our method not only in industrial wood processing applications but also in other industries where materials undergo similar processes of defect detection and segmentation. By utilizing our method, these industries can expect to see improved efficiency, accuracy, and overall productivity.

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“…Statistical method such as [1] uses fuzzy connected components to detect defects on strip steel surfaces. By combining the pixel connectivity, it is possible to find defects by calculating the maximum and sum of fuzzy connected areas.…”

Section: Related Workmentioning

confidence: 99%

Real-time defect detection and classification on wood surfaces using deep learning

Mazhar¹,

Samson²,

Haataja³

et al. 2022

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This paper proposes a novel method for automatic realtime defect detection and classification on wood surfaces. Our method uses deep convolutional neural network (CNN) based approach Faster R-CNN (Region-based CNN ) as detector and Mo-bileNetV3 as backbone network for feature extraction. The key difference of our approach from the existing methods is that it detects knots and other type of defects efficiently and does the classification in real-time from the input video frames. Speed and accuracy is the main focus of our work. In the case of the industrial quality control and inspection such as defects detection, the task of detection and classification needs to be done in real-time on a computationally limited processing units or commodity processors. Our trained model is a light weight, and it can even be deployed on systems for example mobile and edge devices. We have pre-trained the MobileNet V3 on large image dataset for feature extraction. We use Faster R-CNN for detection and classification of defects. The system does the real-time detection and classification on an average of 37 frames per second from input video frames, using low cost and low memory GPU (Graphics Processing Unit). Our method has achieved an overall accuracy of 99% in detecting and classifying defects.

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An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces

Cited by 29 publications

References 35 publications

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Convolutional neural networks for real-time wood plank detection and defect segmentation

Real-time defect detection and classification on wood surfaces using deep learning

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