Recent advances in surface defect inspection of industrial products using deep learning techniques

2021 ◽  
Vol 113 (1-2) ◽  
pp. 35-58
Author(s):  
Xiaoqing Zheng ◽  
Song Zheng ◽  
Yaguang Kong ◽  
Jie Chen
Keyword(s):  
Deep Learning ◽  
Surface Defect ◽  
Defect Inspection ◽  
Industrial Products ◽  
Recent Advances ◽  
Learning Techniques

scholarly journals A deep learning-based surface defect inspection system using multi-scale and channel-compressed features

2020 ◽  
pp. 1-1
Author(s):  
Jiangxin Yang ◽  
Guizhong Fu ◽  
Wenbin Zhu ◽  
Yanlong Cao ◽  
Yanpeng Cao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Defect ◽  
Inspection System ◽  
Defect Inspection ◽  
Multi Scale

Gastronomic Algorithms: Artistic and Sensory Exploration of Alain Passard's Michelin Plates in the Manner of Giuseppe Arcimboldo with GANs

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Emily L. Spratt
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Artistic Collaboration ◽  
Recent Advances ◽  
Learning Techniques ◽  
Data Scientist ◽  
The Senses ◽  
Computer Programmer

Abstract Although recent advances in artificial intelligence to generate images with deep learning techniques, especially generative adversarial networks (GANs), have offered radically new opportunities for its creative applications, there has been little investigation into its use as a tool to explore the senses beyond vision alone. In an artistic collaboration that brought Chef Alain Passard, art historian and data scientist Emily Spratt, and computer programmer Thomas Fan together, photographs of the three-star Michelin plates from the Parisian restaurant Arpège were used as a springboard to explore the art of culinary presentation in the manner of the Renaissance painter Giuseppe Arcimboldo.

scholarly journals AI and Deep Learning for Urban Computing

2021 ◽  
pp. 815-844
Author(s):  
Senzhang Wang ◽  
Jiannong Cao
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Urban Areas ◽  
Urban Computing ◽  
Recent Advances ◽  
Learning Techniques ◽  
Urban Safety ◽  
Significant Performance ◽  
Location Based Social Networks ◽  
Performance Gains

AbstractIn the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing. We first introduce the background, followed by listing key challenges from the perspective of computer science when AI techniques are applied. Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models, deep learning, and reinforcement learning. With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications. Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring. For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.

scholarly journals Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

2018 ◽  
Vol 8 (9) ◽  
pp. 1575 ◽  
Author(s):  
Xian Tao ◽  
Dapeng Zhang ◽  
Wenzhi Ma ◽  
Xilong Liu ◽  
De Xu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
Low Noise ◽  
Machine Learning Techniques ◽  
Metallic Surface ◽  
Defect Inspection ◽  
Learning Techniques ◽  
Defect Image ◽  
Surface Defect Detection

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

scholarly journals Recent Advances in Deep Learning Techniques for Face Recognition

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md. Tahmid Hasan Fuad ◽  
Awal Ahmed Fime ◽  
Delowar Sikder ◽  
Md. Akil Raihan Iftee ◽  
Jakaria Rabbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recent Advances ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document