Cascading Convolutional Neural Network for Steel Surface Defect Detection

Lin, Chih-Yang; Lin, Chun-Yu; Yang, Ching-Yuan; Akhyar, Fityanul; Hsu, Chao-Yung; Ng, Hui-Fuang

doi:10.1007/978-3-030-20454-9_20

Cited by 22 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lin et al. [152] proposed a DL method to detect defects, which used SSD to learn possible defects and ResNet to classify three types of defects on the steel surface. Li et al.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

“…Wei et al [151] proposed a defects detection method based on Faster R-CNN, and showed that the detection rate can reach 97% in real industrial environments. Lin et al [152] proposed a DL method to detect defects, which used SSD to learn possible defects and ResNet to classify three types of defects on the steel surface. Li et al [153] used improved YOLO consisting of 27 convolution layers to detect six types of steel strip surface defects, and its mAP (mean average precision) and recall rate reached 97.55% and 95.86%, respectively, as well as the detection rate reached 99% at 83 fps.…”

Section: Annmentioning

confidence: 99%

See 1 more Smart Citation

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

show abstract

“…Lin et al. [152] proposed a DL method to detect defects, which used SSD to learn possible defects and ResNet to classify three types of defects on the steel surface. Li et al.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The main goal is to achieve the best performance and highest efficiency in the production process. Nowadays, with the rising popularity of deep learning techniques for visual recognition, deep learning-based defect detection has been extensively applied to surface defect inspection systems [3,13,14,27,28,30,38,66].…”

Section: Introductionmentioning

confidence: 99%

FDD: The Data Pipeline for Steel Defect Detectors

Akhyar

Liu²,

Hsu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture, and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point-of-view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of the industrial manufacturing process by increasing productivity and by sustaining high product qualities.

show abstract

“…Steel plays a significant role in society's development, including building construction materials, vehicle parts, infrastructure, and many other fields. However, the production process of steel is quite difficult, due to its very high production temperature [4]. An automatic steel surface defect detection system could help improve steel product quality.…”

Section: Introductionmentioning

confidence: 99%

A Beneficial Dual Transformation Approach for Deep Learning Networks Used in Steel Surface Defect Detection

Akhyar

Lin

Kathiresan

2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

Self Cite

View full text Add to dashboard Cite

Steel surface defect detection represents a challenging task in realworld practical object detection. Based on our observations, there are two critical problems which create this challenge: the tiny size, and vagueness of the defects. To solve these problems, this study a proposes a deep learning-based defect detection system that uses automatic dual transformation in the end-to-end network. First, the original training images in RGB are transformed into the HSV color model to re-arrange the difference in color distribution. Second, the feature maps are upsampled using bilinear interpolation to maintain the smaller resolution. The latest and state-of-the-art object detection model, High-Resolution Network (HRNet) is utilized in this system, with initial transformation performed via data augmentation. Afterward, the output of the backbone stage is applied to the second transformation. According to the experimental results, the proposed approach increases the accuracy of the detection of class 1 Severstal steel surface defects by 3.6% versus the baseline.

show abstract

Cascading Convolutional Neural Network for Steel Surface Defect Detection

Cited by 22 publications

References 8 publications

Review of surface defect detection of steel products based on machine vision

Review of surface defect detection of steel products based on machine vision

FDD: The Data Pipeline for Steel Defect Detectors

A Beneficial Dual Transformation Approach for Deep Learning Networks Used in Steel Surface Defect Detection

Contact Info

Product

Resources

About