Steel Plate Surface Defect Detection Based on Dataset Enhancement and Lightweight Convolution Neural Network

Yang, Luya; Huang, Xinbo; Ren, Yucheng; Huang, Yanchen

doi:10.3390/machines10070523

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…To ensure a more objective comparison of the improved model, we analysed its performance alongside the current mainstream models, including YOLOX, Faster R-CNN, SSD,Resnet-50,YOLOv3, YOLOv4, YOLOv5, YOLOv7-tiny, and YOLOv8. Furthermore, we compared it with several other models, including the CBAM-MobilenetV2-YOLOv5 model (CM-YOLOv5) proposed by Yang [32] et al, the YOLO-ACG model by Wang [33] et al, the AGCN model by Zhang [34] et al, and the improved YOLOv8 model by Wei [35] et al, the multi-scale lightweight neural network model (MM) proposed by Shao [36] et al, and Zhang [37] et al proposed a model that combines CNN and Transformer. The experiments were conducted using identical hardware and software configurations, and the same dataset of steel surface defects was used.…”

Section: Comparative Experiments Of Different Algorithmsmentioning

confidence: 99%

Steel surface defect detection algorithm based on ESI-YOLOv8

Zhang,

Wang,

Fang

2024

Mater. Res. Express

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To enhance the precision of detecting defects on steel plate surfaces and diminish the incidences of false detection and leakage, the ESI-YOLOv8 algorithm is introduced. The algorithm incorporates an EP module, which minimizes redundant computations and model parameters to optimize efficiency and simultaneously increases the multi-scale fusion mechanism to expand the sensory field. The SPPF-LSKA module is proposed by integrating the large separated convolutional attention module with the spatial pyramid pooling module. This integration reduces computational complexity, accelerates model operation speed, and improves detection accuracy. Additionally, the INNER-CIOU loss function is introduced to improve detection speed and model accuracy by controlling the scale size of the auxiliary border. The results of the experiment indicate that, following the improvements made, the algorithm's detection accuracy has increased to 78%, which is 3.7% higher than the original YOLOv8. Furthermore, the model parameters were reduced, and the verification was conducted using the CoCo dataset, resulting in an average accuracy of 77.8%. In conclusion, the algorithm has demonstrated its ability to perform steel plate surface defect detection with efficiency and accuracy.

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Section: Comparative Experiments Of Different Algorithmsmentioning

confidence: 99%

Steel surface defect detection algorithm based on ESI-YOLOv8

Zhang,

Wang,

Fang

2024

Mater. Res. Express

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“…Wang et al 23 proposed a steel surface defect detection model DAssd-Net based on multi-domain perception detection head and multi-branch dilated convolution aggregation, which improves the detection accuracy of the model based on reduced model size. Yang et al 24 proposed a way to improve the YOLOv5 algorithm by introducing the MobilenetV2 module and the CBAM attention mechanism to obtain a more lightweight defect detection model.…”

Section: Deep Learning-based Defect Detectionmentioning

confidence: 99%

Efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet: a high-performance model for precise steel surface defect detection

Su,

Zhang,

Przystupa

et al. 2024

J. Electron. Imag.

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This work proposes efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet (ES-BiCF-YOLOv8), a deep learning method, to address the problems associated with detecting steel surface defects in contemporary industrial production. The method makes innovative improvements based on the YOLOv8 algorithm and enhances the performance of the novel model mainly through the following aspects. First, the space-to-depth layer followed by a non-strided convolution layer (SPD-Conv) and the efficient multi-scale attention mechanism is introduced into the feature extraction network to enhance the model's ability to capture fine-grained information and the fusion of multi-scale features. Second, the feature fusion network is optimized by utilizing a weighted bi-directional feature pyramid network and a lightweight network, FasterNet, to improve computational efficiency. Finally, it is shown that ES-BiCF-YOLOv8 reduces the complexity and computational requirements of the model while increasing the detection accuracy utilizing the NEU-DET dataset and deepPCB dataset with substantial experimental validation. The ES-BiCF-YOLOv8 model achieves a 5% improvement of the mean average precision value on the NEU-DET dataset, with the number of parameters and the computational amount only being the baseline 89% and 27%, and also demonstrates good generalization performance on the deepPCB dataset. Furthermore, the experiments demonstrate that ES-BiCF-YOLOv8 can be used for steel surface defect detection in industrial production because it uses less computational resources and can detect in real-time while maintaining high accuracy, in comparison to other popular object detection algorithms. The results of this work not only improve the efficiency and accuracy of steel surface defect detection but also provide ideas for the application of deep learning in the field of industrial detection.

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“…The latter ranges from 2.30% for MobileNet to 3.13 % for RegNet. Yang et al [8] propose a semi-supervised CNN based approach. An enhancement of the dataset in conjunction with annotation leads to an improvement of the average precision to 0.924.…”

Section: Related Workmentioning

confidence: 99%

Ontology based approach using a systemic knowledge model for surface defect classification

ABBES,

Thebti,

Sellami

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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An understanding of the image starts with sensing pertinent information, and subsequently recognize domain objects, based on a prior conceptualization. Thus, suitable modeling of the image content is essential to make use of the dependency between patterns in a particular domain. Through a computer interpretable model that results in a knowledge-based model, we can optimize the leverage of knowledge in image interpretation of a certain domain. In this paper, we focus on a systemic knowledge modeling intended for surface defect classification. Accordingly, we have exploited the image spatial information for building surface defect domain ontology. A set of statistical texture features has been extracted. A systemic approach of conceptualisation has been proposed, based on a decision tree classification, looking at filling the gap between low and medium level knowledge on the one hand and high level knowledge, which is defect detect categories on the other hand. Accordingly, the proposed ontology has been modeled with OWL and SWRL for reasoning and rule inference. The information, extracted from the grayscale image and its significance for deducing the surface flaws, is formalized to establish surface defect ontology. Validation of the proposed approach has been done on an industrial radio-graphs dataset NEU-DET. Compared to the state-of-the-art, our method yields on the same dataset a challenging performance of 85.87 % in term of mean average precision (mAP).

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Steel Plate Surface Defect Detection Based on Dataset Enhancement and Lightweight Convolution Neural Network

Cited by 14 publications

References 22 publications

Steel surface defect detection algorithm based on ESI-YOLOv8

Steel surface defect detection algorithm based on ESI-YOLOv8

Efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet: a high-performance model for precise steel surface defect detection

Ontology based approach using a systemic knowledge model for surface defect classification

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