• Corpus ID: 235694261

One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map

  title={One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map},
  author={Takato Yasuno and Junichiro Fujii and Sakura Fukami},
For steel product manufacturing in indoor factories, steel defect detection is important for quality control. For example, a steel sheet is extremely delicate, and must be accurately inspected. However, to maintain the painted steel parts of the infrastructure around a severe outdoor environment, corrosion detection is critical for predictive maintenance. In this paper, we propose a general-purpose application for steel anomaly detection that consists of the following four components. The first… 


GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
This work introduces a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space and shows the model efficacy and superiority over previous state-of-the-art approaches.
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This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips.
Research Progress of Visual Inspection Technology of Steel Products—A Review
The purpose of this article is to study the latest developments in steel inspection relating to the detected object, system hardware, and system software, existing problems of current inspection technologies, and future research directions.
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
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This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
Very Deep Convolutional Networks for Large-Scale Image Recognition
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Framework for Structural Health Monitoring of Steel Bridges by Computer Vision
This study presents a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability.
Defect inspection technologies for additive manufacturing
Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various
Computer Vision Techniques in Construction, Operation and Maintenance Phases of Civil Assets: A Critical Review
  • S. Xu, J. Wang, X. Wang, W. Shou
  • Business
    Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC)
  • 2019
This review aims to provide a comprehensive insight about the utilization of computer vision in civil engineering and an inspiring guidance for future research.