Automated Visual Defect Classification for Flat Steel Surface: A Survey

Luo, Qiwu; Fang, Xiaoxin; Su, Jiaojiao; Zhou, Jian; Zhou, Bingxing; Yang, Chunhua; Liu, Li; Gui, Weihua; Lan, Tian

doi:10.1109/tim.2020.3030167

Cited by 92 publications

(42 citation statements)

References 131 publications

(210 reference statements)

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“…Differentiating between similar-looking plastics can be challenging. CNNs consistently demonstrate better performance in extracting visual properties [15]. Deep CNNs can detect transparent object features better with an ability to distinguish between transparent overlapping objects and non-transparent ones with the same shape [16,17,18].…”

Section: Related Workmentioning

confidence: 92%

An Object Detection and Scaling Model for Plastic Waste Sorting

Padalkar¹,

Pathak²,

Stynes³

2021

Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bolog

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Plastic waste sorting involves the separation of plastic into its individual plastic types. This research proposes an Object Detection and Scaling Model for plastic waste sorting to detect four types of plastics using the WaDaBa dataset. This research compares the Object Detection and Scaling Models Scaled-Yolov4 and EfficientDet. Results demonstrate that Scaled-Yolov4-CSP outperforms the state of the art, Colour-Histogram based Canny-Edge-Gaussian Filter, by 21% accuracy.

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Section: Related Workmentioning

confidence: 92%

An Object Detection and Scaling Model for Plastic Waste Sorting

Padalkar¹,

Pathak²,

Stynes³

2021

Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bolog

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“…The interference defect dataset involves two common noises in the actual production environment, which are Gaussian white noise and salt and pepper noise. According to Luo et al [46], due to the high temperature of the image sensor or the lack of illuminance, the occurrence of Gaussian noise will occur during data collection. Besides, the transmission error by the camera will generate random black or white points (salt and pepper noise) and disturb the feature learning progress.…”

Section: Datasets Analysismentioning

confidence: 99%

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

et al. 2021

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With the advancement of industrial intelligence, defect recognition has become an indispensable part of facilitating surface quality in the steel manufacturing process. To assure product quality, most previous studies were typically trained with many defect samples. Nonetheless, a large quantity of defect samples is difficult to obtain, owing to the rare occurrence of defects. In general, deep learning-based methods underperformed as they have inherent limitations due to inadequate information, thereby restraining the application of models. In this study, a two-level Gaussian pyramid is applied to decompose raw data into different resolution levels simultaneously filtering the noises to acquire compact and representative features. Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when trained using a lightweight dataset. In addition, the experiments conducted using the disturbance defect dataset showed the robustness of the proposed MRFFN against common noises and motion blur.

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“…In contrast, deep learning has multilayer perceptron with multiple hidden layers, which can form more abstract category features by combining low-level features. Therefore, the steel surface defect detection method based on deep learning is widely used in steel production, and more scholars begin to improve and perfect it [ 13 , 14 ]. Fu et al [ 15 ] proposed a convolutional neural network model which emphasizes the training of the underlying features and realized the rapid and accurate classification of steel surface defects by combining with multiple receptive fields.…”

Section: Introductionmentioning

confidence: 99%

A New Steel Defect Detection Algorithm Based on Deep Learning

Zhao

Feng

Huang

et al. 2021

Computational Intelligence and Neuroscience

153

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In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

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Automated Visual Defect Classification for Flat Steel Surface: A Survey

Cited by 92 publications

References 131 publications

An Object Detection and Scaling Model for Plastic Waste Sorting

An Object Detection and Scaling Model for Plastic Waste Sorting

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

A New Steel Defect Detection Algorithm Based on Deep Learning

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