Journal ArticleDOI
A deep-learning-based approach for fast and robust steel surface defects classification
TLDR
A compact yet effective convolutional neural network model, which emphasizes the training of low-level features and incorporates multiple receptive fields, to achieve fast and accurate steel surface defect classification and adopts the pre-trained SqueezeNet as the backbone architecture.About:
This article is published in Optics and Lasers in Engineering.The article was published on 2019-10-01. It has received 133 citations till now. The article focuses on the topics: Convolutional neural network & Deep learning.read more
Citations
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Journal ArticleDOI
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.
TL;DR: With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.
Journal ArticleDOI
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.
TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.
Journal ArticleDOI
A Deep Learning-Based Surface Defect Inspection System Using Multiscale and Channel-Compressed Features
TL;DR: This work proposes to incorporate multiple convolutional layers with different kernel sizes to increase the receptive field and to generate multiscale features in a surface defect data set and achieves more accurate recognition results compared with the state-of-the-art surface defect classifiers.
Journal ArticleDOI
Image-Based Surface Defect Detection Using Deep Learning: A Review
Prahar M. Bhatt,Rishi K. Malhan,Pradeep Rajendran,Brual C. Shah,Shantanu Thakar,Yeo Jung Yoon,Satyandra K. Gupta +6 more
Journal ArticleDOI
A New Steel Defect Detection Algorithm Based on Deep Learning.
TL;DR: 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 algorithms in steel surface defect detection.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).