An improvement and application of a model conducive to productivity optimization

Kou, Xupeng; He, Ying; Ye, Qian

doi:10.1109/icpeca51329.2021.9362627

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…In industries, datasets are generally proprietary and are not exposed to researchers, due to the costs of generating them, for example, BS5-DET [6], but it is still possible to find datasets already created and for open use, like the DAGM dataset [25] or the COCO dataset [26]. Most free datasets are found in Kaggle [27].…”

Section: Resultsmentioning

confidence: 99%

“…Metal surfaces are the group with the most subtypes because metals can be used in their pure state, in alloys such as steel (the most used metal in this systematic review), or in interesting surfaces such as titanium-coated metal [31], microscopic images from thin metal film in electronic components [67], semiconductor wafers (from the metal layers) [71], polishing metal shafts [29], car wiper arms [58], microscopic metal parts [62], cuts from laser cutting machines [64], wind turbine blades [66], insulators in the transmission line aims [68], and X-ray images from metals [72]. Most of the datasets are created by the authors and are kept private; however, some are free to use like BS5-DET [6], CSU_STEEL [41], and GC10-DET [46]. For studies that aim to compare the proposed methods with traditional networks or their datasets such as [41,46,50,53,57,84], the most commonly used option for metal is NEU-DET [28], which has been tested and contains six of the most common defects (crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches).…”

Section: Metalmentioning

confidence: 99%

“…The most popular algorithms are grounded on deep learning methods because they are based on input data, so they automatically learn the characteristics of the defects. Conversely, traditional detection technology is based on human labor, so the difference is that current methods reduce labor consumption [6]. Defect detection is generally carried out on images of a dataset from a camera, but it can also be developed using lamb wave data converted images such as in [7] or through sensors such as in [8], where the authors obtain C-scan images from an anisotropic magneto-resistive (AMR) sensor.…”

Section: Introductionmentioning

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: Resultsmentioning

confidence: 99%

Section: Metalmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Non-destructive techniques and photogrammetry have been used for the inspection of brick buildings, but manual inspection is not time-effective, and it can lead to errors. The presented detection methods reduce the time needed for the work to be completed [10]. Transfer learning was used to address this gap by classifying brickwork images as cracked or normal.…”

Section: Related Workmentioning

confidence: 99%

Building Surface Defect Detection Using Machine Learning and 3D Scanning Techniques in the Construction Domain

Mariniuc,

Cojocaru,

Abagiu

2024

Buildings

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The rapid growth of the real estate market has led to the appearance of more and more residential areas and large apartment buildings that need to be managed and maintained by a single real estate developer or company. This scientific article details the development of a novel method for inspecting buildings in a semi-automated manner, thereby reducing the time needed to assess the requirements for the maintenance of a building. This paper focuses on the development of an application which has the purpose of detecting imperfections in a range of building sections using a combination of machine learning techniques and 3D scanning methodologies. This research focuses on the design and development of a machine learning-based application that utilizes the Python programming language and the PyTorch library; it builds on the team′s previous study, in which they investigated the possibility of applying their expertise in creating construction-related applications for real-life situations. Using the Zed camera system, real-life pictures of various building components were used, along with stock images when needed, to train an artificial intelligence model that could identify surface damage or defects such as cracks and differentiate between naturally occurring elements such as shadows or stains. One of the goals is to develop an application that can identify defects in real time while using readily available tools in order to ensure a practical and affordable solution. The findings of this study have the potential to greatly enhance the availability of defect detection procedures in the construction sector, which will result in better building maintenance and structural integrity.

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“…Currently, manual and visual inspections performed by experts are expensive due to the high cost of human labor during working hours, the possibility of material waste, and the degraded quality of shipped products [ 1 ]. In contrast, the use of machine learning algorithms for automatic defect detection reduces labor consumption [ 2 ]. In recent years, automatic defect detection has played a critical role in the industry’s inspection process.…”

Section: Introductionmentioning

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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An improvement and application of a model conducive to productivity optimization

Cited by 3 publications

References 7 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

Building Surface Defect Detection Using Machine Learning and 3D Scanning Techniques in the Construction Domain

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

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