scholarly journals On-machine surface defect detection using light scattering and deep learning

2020 ◽  
Vol 37 (9) ◽  
pp. B53 ◽  
Author(s):  
Mingyu Liu ◽  
Chi Fai Cheung ◽  
Nicola Senin ◽  
Shixiang Wang ◽  
Rong Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Scattering ◽  
Defect Detection ◽  
Machine Surface ◽  
Surface Defect ◽  
Surface Defect Detection

Aluminum Surface-Defect Detection Based on Multi-Task Deep Learning

2020 ◽  
Vol 57 (10) ◽  
pp. 101501
Author(s):  
沈晓海 Shen Xiaohai ◽  
栗泽昊 Li Zehao ◽  
李敏 Li Min ◽  
徐晓龙 Xu Xiaolong ◽  
张学武 Zhang Xuewu
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Aluminum Surface ◽  
Surface Defect Detection

Online metallic surface defect detection using deep learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1266-1273
Author(s):  
Feyza Cerezci ◽  
Serap Kazan ◽  
Muhammed Ali Oz ◽  
Cemil Oz ◽  
Tugrul Tasci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Metallic Surface ◽  
Surface Defect Detection

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

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.

scholarly journals PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 221
Author(s):  
Linjian Lei ◽  
Shengli Sun ◽  
Yue Zhang ◽  
Huikai Liu ◽  
Wenjun Xu
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Performance Metrics ◽  
Surface Defects ◽  
Training Data ◽  
Detection Methods ◽  
Detection Technology ◽  
Surface Defect Detection ◽  
Public Datasets

Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net.

Ceramic tile surface defect detection based on deep learning

2021 ◽  
Author(s):  
Guang Wan ◽  
Hongbo Fang ◽  
Dengzhun Wang ◽  
Jianwei Yan ◽  
Benliang Xie
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Ceramic Tile ◽  
Surface Defect ◽  
Surface Defect Detection ◽  
Tile Surface

Smooth Surface Defect Detection by Deep Learning Based on Wrapped Phase Map

2021 ◽  
pp. 1-1
Author(s):  
Jiamin Tao ◽  
Yongjian Zhu ◽  
Wenyi Liu ◽  
Frank Jiang ◽  
Hongzhan Liua
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Smooth Surface ◽  
Surface Defect ◽  
Surface Defect Detection ◽  
Phase Map

scholarly journals Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm

2019 ◽  
Vol 7 (4.14) ◽  
pp. 401
Author(s):  
Ze-Hao Wong ◽  
C. M. Thong ◽  
W. M. Edmund Loh ◽  
C. J. Wong
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Linear Filter ◽  
Wide Range ◽  
Surface Defect Detection ◽  
Design Variation ◽  
Industrial Lighting

Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.  

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