scholarly journals Image Media Applications in New Industrial Production Paradigm. Image Information and Madia Technology in Automating Visual Inspection.

1998 ◽  
Vol 52 (5) ◽  
pp. 647-652
Author(s):  
Yasuhiko Hara
Keyword(s):  
Industrial Production ◽  
Visual Inspection ◽  
Image Information ◽  
Media Applications

scholarly journals A Data Augmentation Method for Deep Learning Based on Multi-Degree of Freedom (DOF) Automatic Image Acquisition

2020 ◽  
Vol 10 (21) ◽  
pp. 7755 ◽  
Author(s):  
Liangliang Chen ◽  
Ning Yan ◽  
Hongmai Yang ◽  
Linlin Zhu ◽  
Zongwei Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Industrial Production ◽  
Visual Inspection ◽  
Data Augmentation ◽  
Image Acquisition ◽  
Degree Of Freedom ◽  
Training Data ◽  
Learning Technology ◽  
Application Process ◽  
Comparison Experiment

Deep learning technology is outstanding in visual inspection. However, in actual industrial production, the use of deep learning technology for visual inspection requires a large number of training data with different acquisition scenarios. At present, the acquisition of such datasets is very time-consuming and labor-intensive, which limits the further development of deep learning in industrial production. To solve the problem of image data acquisition difficulty in industrial production with deep learning, this paper proposes a data augmentation method for deep learning based on multi-degree of freedom (DOF) automatic image acquisition and designs a multi-DOF automatic image acquisition system for deep learning. By designing random acquisition angles and random illumination conditions, different acquisition scenes in actual production are simulated. By optimizing the image acquisition path, a large number of accurate data can be obtained in a short time. In order to verify the performance of the dataset collected by the system, the fabric is selected as the research object after the system is built, and the dataset comparison experiment is carried out. The dataset comparison experiment confirms that the dataset obtained by the system is rich and close to the real application environment, which solves the problem of dataset insufficient in the application process of deep learning to a certain extent.

Special Issue on Visual Inspection

1993 ◽  
Vol 5 (2) ◽  
pp. 87-87
Author(s):  
Masanori Idesawa ◽  
Keyword(s):  
Visual Function ◽  
Information Acquisition ◽  
Visual Information ◽  
Visual Inspection ◽  
Image Sensor ◽  
Human Vision ◽  
Sensor Technology ◽  
Special Issue ◽  
Image Information ◽  
Wide Range

We acquire more than 60 percent of information from our activity environment through our visual sense. The visual sense allows us to collect information about an object from a position away from it without exerting any effects it such as constraining its motion. Visual information acquisition plays a very important role in the industrial field including visual appearance inspection and various other monitoring. A field called machine vision or computer vision has been formed, it is related to the artificial realization and application of the visual function and is now under aggressive study. Inspection using the visual sense, so-called visual inspection, is extremely important; and its automation has been studied for a long time. However, many problems remain to be solved; and in many cases, this operation must rely on human vision. In order to realize the visual function from an engineering point of view, there are many demands for the development of an image sensor that acquires visual information as image information, a method that processes and recognizes image information, and a method that integrates the observation control system allowing processed image information to be systematically organized and the operation to be checked. In consideration of long-term vision as stated above, this special issue provides a description of sensor technology for image information acquisition in the visual inspection process as well as the neural network processing method which is expected as a flexible method for image processing and recognition. For robot sensors, an active method is used to simplify the recognition process, which projects a special light on an object for measurement. This issue includes the topics covering the development of sensors, aiming at their downsizing and high performance. The human visual sense may function by two operating modes: the monitoring mode that senses an unusual situation appearing in the view field and the attention mode that provides detailed analysis of the situation in this area. The former is permitted to have a low detecting, accuracy, but it requires a wide detectable range. The latter is permitted to have a narrow sensing range, but it requires a high sensing accuracy. In other words, multi-resolution sensing operations are performed in the human visual sense. It is desirable for robot sensors to perform the multi-resolution operations that enable coarse sensing to be realized in a wide range and high-accuracy sensing in the attentive area. This issue also includes the development of these sensors. The appearance inspection of welded boats and the recognition of vehicle numbers have been put to practical use, and these topics are also described in this issue. In some cases, techniques visual information processing can make visible to us those that can not be seen by our visual system. This can be thought as an extension of the visual function and the level of sight is very interesting.

Effect of alternate feedback strategies on performance for a synthetic aircraft visual inspection task

2003 ◽  
Author(s):  
Rahul R. Desai ◽  
Anand K. Gramopadhye ◽  
Brian J. Melloy ◽  
Andrew Duchowski
Keyword(s):  
Visual Inspection ◽  
Feedback Strategies ◽  
Inspection Task

Industrial Output in the USSR and Russia, 1861—2012. Part II. Crises and Cycles

2013 ◽  
pp. 138-153 ◽  
Author(s):  
S. Smirnov
Keyword(s):  
Industrial Production ◽  
Turning Points ◽  
Russian Empire ◽  
Industrial Output ◽  
Production Index ◽  
Russian Industry ◽  
Industrial Production Index ◽  
Cyclical Volatility

Calculation of the aggregated "consensus" industrial production index has made it possible to date cyclical turning points and to measure the depth and length of the main industrial recessions in Russian Empire/USSR/Russia for the last century and a half. The most important causes of all these recessions are described. The cyclical volatility of Soviet/Russian industry is compared to that of American one.

Sign in / Sign up

Export Citation Format

Share Document