Automated Surface Defects Detection on Mirrorlike Materials by using Faster R-CNN

Karangwa, Jules; Kong, Linghua; You, Tongfei; Zheng, Jiaojiao

doi:10.1109/icisce50968.2020.00341

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Defects like breaks, cracks, pinholes, dirt, pits, and spots, shown in [40], are repeated in this material; therefore, they can be used as a guide for future studies. The datasets have not been released because we work with factories, so the information is kept private.…”

Section: Ceramicmentioning

confidence: 92%

“…Min et al [32] proposed the use of CNNs (ResNet20, ResNet56, and ResNet110) in defect detection for ceramic images with data augmentation. Karangwa et al [40] present a proposal for surface defect detection based on a Faster R-CNN with VGG16. However, with custom CNNs, Birlutiu et al [82] presented an automated defect management system with real-time high-speed processing to classify and predict images with and without defects.…”

Section: Ceramicmentioning

confidence: 99%

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A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Cumbajin,

Rodrigues,

Costa

et al. 2023

J. Imaging

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Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.

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Section: Ceramicmentioning

confidence: 92%

Section: Ceramicmentioning

confidence: 99%

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Cumbajin,

Rodrigues,

Costa

et al. 2023

J. Imaging

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“…[23] comprehensively compared the performance of CNN, SAE, MP and SVM-RBF based on the data set of wafer surface defects, and concluded that CNN was superior to other models, and further proposed an automatic defect classification system based on CNN. As traditional defect detection methods rely on manual extraction and have limited application occasions, [24] uses deep learning Faster RCNN model and VGG16 as feature extractor to detect mirrorlike material defects with 94% accuracy. [25] proposed a classification and recognition method for surface defects of sheet metal parts with small samples based on CNN model, built a classic CNN model and fine-tuned parameters, and used data augmentation technology to expand the number of samples.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Features need to be extracted manually; The robustness of the system is poor; Most of the system cannot achieve end-to-end structure. SVM [7,[16][17][18]21] RF [13,20] Deep Learning CNN [23][24][25][28][29][30] Deep extraction of effective feature information; Good robustness; High efficiency.…”

Section: Concrete Model or Algorithm Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Deeply Optimized Convolutional Neural Networks and Research in Surface Defect Classification of Workpieces

Luo

Chen³

et al. 2022

IEEE Access

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Currently, the industrial development is becoming increasingly rapid. Technicalization, informatization and industrialization gives the fundamental impetus for industrial development and progress. Nevertheless, there are numerous problems that hindering industrial progress and human security in the industrial field. The surface defects of the workpieces are one of the primary problems. Moreover, defects of multi-type, mixed and unapparent characteristics presented by workpieces make the detection and classification of workpiece more difficult. Deep convolutional neural networks (DCNN) show strong ability of feature extraction and mines deeper essential features of data because of its features of unique receptive field structure and weights of shared. It can represent original data information well and obtain results more accurately than the traditional methods. But there also remains a problem that conventional DCNN has a huge number of parameters and computation, which brings great pressure to the equipment in terms of computing power, memory, speed and so on. Based on this situation, the optimization methods of CNNs model in the aspects of data, structure, algorithm are summarized. Related lightweight structures and networks are also summarized in this paper. The purpose of these work is to reduce the number of parameters and computation and improve the training performance. At the same time, the research on defect classification of workpieces based on traditional machine learning and deep learning model is reviewed, and the research on defect classification of workpieces based on deeply optimized CNNs is referred and prospected.

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“…We chose three of the most extensively used neural networks based on our systematic review [5] and the recommendations provided by Kermanidis et al [23]. Although there is a lack of studies focused on ceramic pieces, it is worth noting that ResNet [15] and VGG [24] have been successfully implemented by researchers. Additionally, we included AlexNet in our selection due to its abundance of information and studies showcasing successful defect detection.…”

Section: Network Architecturementioning

confidence: 99%

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning

Cumbajin,

Rodrigues,

Costa

et al. 2023

Sensors

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Defect detection is a key element of quality control in today’s industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.

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Automated Surface Defects Detection on Mirrorlike Materials by using Faster R-CNN

Cited by 5 publications

References 17 publications

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

An Overview of Deeply Optimized Convolutional Neural Networks and Research in Surface Defect Classification of Workpieces

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning

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