Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map

Lee, Soo Young; Tama, Bayu Adhi; Moon, Seok Jun; Lee, Seung−Chul

doi:10.3390/app9245449

Cited by 59 publications

(31 citation statements)

References 24 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The energy efficiency was improved by about 29% [92]. Lee et al [93] applied CNN for product quality control of steel. Thus, defects can be detected early on within the manufacturing system, and processes can be adapted.…”

Section: Resultsmentioning

confidence: 99%

“…-Predictive Maintenance [57,59,69,70,80,83,84,86,95,113]; -Production planning [52,54,61,65,72,77,78,101,102]; -Fault detection and prediction/predictive quality [58,62,74,82,87,89,93,94,110,111,115,118]; -Increasing energy efficiency in production [56,63,85,99,100,103,108,114,119] and facility management [53,67,76,107,109].…”

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Of all the algorithms used, it produces good accuracy in classifying steel defects. S. Y. Lee et al [8] and J. L. Greece et al [9] have also used deep learning and CNN in their research to detect steel defects.…”

Section: Related Workmentioning

confidence: 99%

Steel Surface Defect Detection using Deep Learning

Fadli¹,

Herlistiono²

2020

IJISRT

View full text Add to dashboard Cite

Steel defects are a frequent problem in steel companies. Proper quality control can reduce quality problems arising from steel defects. Nowadays, steel defects can detect by automation methods that utilize certain algorithms. Deep learning can help the steel defect detection algorithm become more sophisticated. In this study, we use deep learning CNN with Xception architecture to detect steel defects from images taken from high-frequency and high-resolution cameras. There are two techniques used, and both produce respectively 0.94% and 0.85% accuracy. The Xception architecture used in this case shows optimal and stable performance in the process and its results.

show abstract

“…In the past decades, researchers have developed a variety of algorithms to detect defects on steel surfaces [2][3][4][5][6][7][8][9][10][11][12][13][14]. One is the traditional methods is based on statistical information and image features.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Wang

Xia

et al. 2021

Metals

120

View full text Add to dashboard Cite

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

show abstract

Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map

Cited by 59 publications

References 24 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Steel Surface Defect Detection using Deep Learning

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Contact Info

Product

Resources

About