A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection

Qiu, Lingteng; Wu, Xiaojun; Yu, Zhiyang

doi:10.1109/access.2019.2894420

Cited by 83 publications

(33 citation statements)

References 15 publications

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“…As we can see that our proposed method has the highest AR and Mean IOU scores. The AR score is 0.69, which is slightly better than that of Qiu et al [46], and greatly higher than the performance of ViDi [52] and FCN [42]. Similarly, in terms of Mean IOU, ours is 0.8450, better than all other methods with a big margin.…”

Section: Performance Metrics For Defect Detectionmentioning

confidence: 62%

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Deep Regression Neural Network for Industrial Surface Defect Detection

Liu

2020

IEEE Access

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Industrial product surface defect detection is very important to guarantee high product quality and production efficiency. In this work, we propose a regression and classification based framework for generic industrial defect detection. Specifically, the framework consists of four modules: deep regression based detection model, pixel-level false positive reduction, connected component analysis and deep network for defect type classification. To train the detection model, we propose a high performance deep network structure and an algorithm to generate label data to capture the defect severity information from data annotation. We have tested the method on two public benchmark datasets, AigleRN and DAGM2007, and an in-house capacitor image dataset. The results have shown that our method can achieve the state-of-the-art performance in terms of detection accuracy and efficiency. INDEX TERMS Deep convolutional neural networks, regression, surface defect detection.

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Section: Performance Metrics For Defect Detectionmentioning

confidence: 62%

“…The image size is 512 × 512. We follow the work of [46] to select the first six classes to conduct the experiment. So, a total of 900 defective images are selected, where 720 images for training and the rest 180 for testing.…”

Section: B Datasetsmentioning

confidence: 99%

Deep Regression Neural Network for Industrial Surface Defect Detection

Liu

2020

IEEE Access

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“…An increasing number of researchers choose to apply deep learning to the detection of defects in production and life, but it has not yet been applied to the detection of surface defects on bearing covers. L. T. Qiu et al [22] applied deep learning to pixel-level surface defect detection. Taking FCN as a basic network, they proposed a defect segmentation algorithm with three stages: segmentation stage, detection stage and matting stage.…”

Section: A Related Workmentioning

confidence: 99%

Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

Zheng

Zhao

2021

IEEE Access

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“…After training the CNN model, the defects and background images from the segmentation step can be input into the CNN to perform the classification task. Qiu et al [ 15 ] proposed an efficient deep learning-based pixel-wise surface defect segmentation algorithm, which consists of a lightweight Fully Convolutional Network (FCN) to make a pixel-wise prediction of the defect areas. Then, a guided filter is used to refine the contour of the defect area to reflect the real abnormal region.…”

Section: Literature Reviewmentioning

confidence: 99%

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Tsai

Chen

2020

Sensors

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This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.

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A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection

Cited by 83 publications

References 15 publications

Deep Regression Neural Network for Industrial Surface Defect Detection

Deep Regression Neural Network for Industrial Surface Defect Detection

Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

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