Development of automatic optical inspection platformfor real-time surface defects detection on planeoptical components based on Semantic Segmentation

2021 ◽  
Author(s):  
Jules KARANGWA ◽  
Linghua Kong ◽  
Dingrong Yi ◽  
Jishi zheng
Keyword(s):  
Real Time ◽  
Surface Defects ◽  
Semantic Segmentation ◽  
Automatic Optical Inspection ◽  
Optical Inspection

An accelerating CPU based correlation-based image alignment for real-time automatic optical inspection

2016 ◽  
Vol 49 ◽  
pp. 207-220 ◽  
Author(s):  
Chin-Sheng Chen ◽  
Chien-Liang Huang ◽  
Chun-Wei Yeh ◽  
Wen-Chung Chang
Keyword(s):  
Real Time ◽  
Image Alignment ◽  
Automatic Optical Inspection ◽  
Optical Inspection

The Synergy of TRIZ and Automatic Optical Inspection (AOI) for Detecting Surface Defects on Small Metal Parts

2013 ◽  
Vol 838-841 ◽  
pp. 2030-2033 ◽  
Author(s):  
Chi Hao Yeh ◽  
Cheng Yu Hsieh ◽  
Ful Chiang Wu
Keyword(s):  
Energy Saving ◽  
Surface Defects ◽  
Metal Part ◽  
Automatic Optical Inspection ◽  
Metal Parts ◽  
Optical Inspection ◽  
Engineering And Technology ◽  
Innovative Thinking

The aim of this paper is to show how to apply TRIZ to resolve difficulties and conflicts in Automatic Optical Inspection (AOI) for detecting surface defects on small metal parts and saving effort-spent (energy saving). TRIZ has been well-known as a creative and innovative thinking theory in solving engineering and technology contradictions in the last two decades. However, few studies and practical usage were proposed in AOI area. Conflicts occurring in AOI are discussed to demonstrate the ideas guided by 39 TRIZ management parameters, 40 innovative principles, and contradiction matrix. The results show that TRIZ is able to provide direct, quick and effective alternatives to resolve the conflicts in AOI. In this manner, huge effort and cost can be saved in further execution stage. In this paper, the detection for surface defects on small and precise metal part are utilized as a case study to describe the proposed TRIZ-based conflicts-solution in AOI implementation.

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

A lightweight network with attention decoder for real-time semantic segmentation

2021 ◽  
Author(s):  
Kang Wang ◽  
Jinfu Yang ◽  
Shuai Yuan ◽  
Mingai Li
Keyword(s):  
Real Time ◽  
Semantic Segmentation

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals Method for detecting surface defects of runner blades of large hydraulic turbines based on improved real-time lightweight network

2021 ◽  
Vol 1955 (1) ◽  
pp. 012090
Author(s):  
Cheng Liu ◽  
XinYi Su ◽  
Jialing Wu ◽  
Qun Zhou ◽  
Tao Li ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Defects ◽  
Hydraulic Turbines

scholarly journals Real-time Semantic Segmentation with Context Aggregation Network

2021 ◽  
Vol 178 ◽  
pp. 124-134
Author(s):  
Michael Ying Yang ◽  
Saumya Kumaar ◽  
Ye Lyu ◽  
Francesco Nex
Keyword(s):  
Real Time ◽  
Semantic Segmentation
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