Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

Liu, Xinbo; Ma, Jianzhong; Su, Wenzhi; Li, Han

doi:10.3390/pr11051357

Cited by 23 publications

(13 citation statements)

References 67 publications

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“…F. Akhyar et al's [6] forceful steel defect detector combined Cascade R-CNN with advanced techniques, exhibiting superior defect detection capabilities in complex environments. Ling Wang et al [9] enhanced YOLOv5 with a multi-scale block and spatial attention mechanism that excelled in real-time defect detection. Yu Zhang et al's [7] integration of the CBAM mechanism into YOLOv5s specifically addressed bottom surface defects in lithium batteries, offering significant detection improvements.…”

Section: Related Workmentioning

confidence: 99%

“…The reviewed papers collectively emphasize the critical role of surface defect detection in materials like steel, metal [3][4][5][6][8][9][10][11] and lithium batteries [7] for industrial quality control, primarily through the use of advanced deep learning models, particularly convolutional neural networks (CNNs). This shift towards AI-based approaches is evident in their experimentation with YOLO (You Only Look Once) and R-CNN (region-based convolutional neural networks) model variants [4][5][6][8][9][10][11], affirming their effectiveness in object detection. Notably, each study introduces enhancements to models such as YOLOv5, Faster R-CNN, or YOLOv7, focusing on increasing accuracy and detection speed, especially for small defects [4,5,8].…”

Section: Related Workmentioning

confidence: 99%

“…As the focus of this work is not the development of a new defect detection model, it uses a variant of the commonly deployed YOLOv5 model. A large number of recent works use YOLOv5 as their basis [3,[5][6][7][8][9][10], and more recently YOLOv7 [4]. Although YOLOv7 outperforms YOLOv5 in terms of performance, this is only possible on high-performance GPUs, which may not be easily accessible to industry.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study

Rattanaphan,

Briassouli

2024

Processes

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In recent years, deep learning models have led to improved accuracy in industrial defect detection, often using variants of YOLO (You Only Look Once), due to its high performance at a low cost. However, the generalizability, fairness and bias of their outcomes have not been examined, which may lead to overconfident predictions. Additionally, the complexity added by co-occurring defects, single and multi-class defects, and the effect on training, is not taken into consideration. This study addresses these critical gaps by introducing new methodologies for analyzing dataset complexity and evaluating model fairness. It introduces the novel approach of co-occurrence impact analysis, examining how the co-occurrence of defects in sample images affects performance, and introducing new dimensions to dataset preparation and training. Its aim is to increase model robustness in the face of real-world scenarios where multiple defects often appear together. Our study also innovates in the evaluation of model fairness by adapting the disparate impact ratio (DIR) to consider the true positive rate (TPR) across different groups and modifying the predictive parity difference (PPD) metric to focus on biases present in industrial quality control. Experiments demonstrate by cross-validation that the model trained on combined datasets significantly outperforms others in accuracy without overfitting and results in increased fairness, as validated by our novel fairness metrics. Explainability also provides valuable insights on the effects of different training regimes, notably absent in prior works. This work not only advances the field of deep learning for defect detection but also provides a strategic framework for future advancements, emphasizing the need for balanced datasets and considerations of ethics, fairness, bias and generalizability in the deployment of artificial intelligence in industry.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study

Rattanaphan,

Briassouli

2024

Processes

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“…YOLOv5, recognized as one of the most extensively employed detection networks, finds application across a range of industries and use cases. These include production processes [11], autonomous driving [12], monitoring and safety [13], surface defect detection [14,15], as well as target detection [16] in various industries and applications. The YOLOv5 network can realize high object detection accuracy and good inference speed.…”

Section: Yolov5mentioning

confidence: 99%

Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing

Mun,

Kim,

et al. 2023

Processes

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According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in determining a company’s competitive advantage. In particular, lead taps, which electrically connect the negative and positive electrodes of secondary batteries, are a key factor in determining the stability of the battery. Currently, the quality inspection of secondary battery lead tab manufacturers mostly consists of visual inspection after vision inspection with a rule-based algorithm, which has limitations on the types of defects that can be detected, and the inspection time is increasing due to overlapping inspections, which is directly related to productivity. Therefore, this study aims to automate the quality inspection of lead tabs of secondary batteries by applying deep-learning-based algorithms to improve inspection accuracy, improve reliability, and improve productivity. We selected the YOLOv5 model, which, among deep-learning algorithms, has a benefit for object detection, and used the YOLOv5_CBAM model, which replaces the bottleneck part in the C3 layer of YOLOv5 with the Convolutional Block Attention Module (CBAM) based on the attention mechanism, to improve the accuracy and speed of the model. As a result of applying the YOLOv5_CBAM model, we found that the parameter was reduced by more than 50% and the performance was improved by 2%. In addition, image processing was applied to help segment the defective area to apply the SPEC value for each defective object after detection.

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“…By combining the Acmix attention mechanism with the GhostNetV2 module, they effectively optimized the defect detection performance. Ling Wang [17] optimized the detection performance of YOLOv5 by using multi-scale detection blocks combined with an attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

GDCP-YOLO: Enhancing Steel Surface Defect Detection Using Lightweight Machine Learning Approach

Yuan,

Ning,

Tang

et al. 2024

Electronics

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Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors.

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Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

Cited by 23 publications

References 67 publications

Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study

Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study

Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing

GDCP-YOLO: Enhancing Steel Surface Defect Detection Using Lightweight Machine Learning Approach

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