Dynamic range in automated visual web inspection

Laitinen, Jyrki

doi:10.1117/1.601617

Cited by 4 publications

(1 citation statement)

References 12 publications

(13 reference statements)

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“…It describes the understanding and interpretation of technically obtained images for controlling production processes [1,2] . Currently, machine vision is widely used in industrial manufacturing, especially in quality detection, such as web [3,4] , bottle [5] , metallic strips [6] , fabrication [7] , glass manufacture [8,9] , etc. Ref.…”

Section: Introductionmentioning

confidence: 99%

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

Chen

Liu

You

et al. 2016

Infrared Physics & Technology

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Intelligent on-line detection of cable quality is a crucial issue in optic cable factory, and defects on the surface of optic cable can dramatically depress cable grade. Manual inspection in optic cable quality cannot catch up with the development of optic cable industry due to its low detection efficiency and huge human cost. Therefore, real-time is highly demanded by industry in order to replace the subjective and repetitive process of manual inspection. For this reason, automatic cable defect inspection has been a trend. In this paper, a novel method for surface defect inspection of optic cable with shortwave infrared illuminance is presented. The special condition of shortwave infrared can not only provide illumination compensation for the weak illumination environment, but also can avoid the problem of exposure when using visible light illuminance, which affects the accuracy of inspection algorithm. A series of image processing algorithms are set up to analyze cable image for the verification of real-time and veracity of the detection method. Unlike some existing detection algorithms which concentrate on the characteristics of defects with an active search way, the proposed method removes the non-defective areas of the image passively at the same time of image processing, which reduces a large amount of computation. OTSU algorithm is used to convert the gray image to the binary image. Furthermore, a threshold window is designed to eliminate the fake defects, and the threshold represents the considered minimum size of defects. Besides, a new regional suppression method is proposed to deal with the edge burrs of the cable, which shows the superior performance compared with that of Open-Close operation of mathematical morphological in the boundary processing. Experimental results of 10000 samples show that the rates of miss detection and false detection are 2.35% and 0.78% respectively when equals to 0.5mm, and the average processing period of one frame image is 2.39ms. All the improvements have been verified in the paper to show the ability of our inspection method for optic cable.

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Section: Introductionmentioning

confidence: 99%

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

Chen

Liu

You

et al. 2016

Infrared Physics & Technology

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An online defects inspection method for float glass fabrication based on machine vision

Xiang-qian

Chen

et al. 2007

Int J Adv Manuf Technol

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Evaluation of solid-state camera systems in varying illumination conditions

Laitinen¹,

Saviaro

Ailisto

2001

Opt. Eng

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We propose a practical method for the expeditious determination of the performance of imaging CCD and CMOS camera systems. Special attention is paid to the operation of these devices in varying illumination conditions, which is typical to many surveillance and consumer electronics applications. One emerging application utilizing imaging sensors in variable illumination conditions is the third generation mobile phone, which will deliver wirelessly pictures, graphics, and video. In our method, determination of the system performance is based on the imaging of a calibrated gray scale test chart as a function of illuminance. At each level of illumination the system response is characterized by a signal to random noise figure. The signal is calculated as the difference of the system response to the lightest and darkest areas of the gray scale. The random noise is measured as the standard deviation of the gray values in a difference of two successive images of the test pattern. The proposed method is applied in three exemplary cases: 1. comparison of inexpensive CCD and CMOS cameras, 2. analysis of the effect of wireless image transmission, and 3. comparison of the effects of automatic and manual gain controls.

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Dynamic range in automated visual web inspection

Cited by 4 publications

References 12 publications

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

An online defects inspection method for float glass fabrication based on machine vision

Evaluation of solid-state camera systems in varying illumination conditions

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