Automatic illumination planning for robot vision inspection system

Wang, Hesheng; Wang, Jingchuan; Chen, Weidong; Xu, Lifei

doi:10.1016/j.neucom.2017.05.015

Cited by 23 publications

(7 citation statements)

References 12 publications

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“…When an object receives a sufficient amount of light in illumination, it will appear in its original color because it will reflect sufficiently [15]. In this respect, ambient lighting, the direction and amount of light are important issues in terms of image quality, accurate detection of objects and energy use [16,17]. In order to obtain successful results and save energy in image processing, a minimum ambient light level must be provided.…”

Section: Introductionmentioning

confidence: 99%

“…In traditional methods, ambient lighting is achieved by continuously turning on the light source, manually turning it on and off as needed, using a timer, dimmer or automatically measuring the illuminance level with the help of various sensors [16,[21][22][23][24][25]. According to the literature research, it is observed that industrial applications based on image processing for lighting and robotic systems are rapidly becoming widespread.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications

Akış,

Dişlitaş

2024

Applied Sciences

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In applications reliant on image processing, the management of lighting holds significance for both precise object detection and efficient energy utilization. Conventionally, lighting control involves manual switching, timed activation or automated adjustment based on illuminance sensor readings. This research introduces an embedded system employing image processing methodologies for intelligent ambient lighting, focusing specifically on reference-color-based illumination for object detection and positioning within robotic handling scenarios. Evaluating the system’s efficacy entails analyzing the illuminance levels and power consumption through a tailored experimental setup. To minimize illuminance, the LED-based lighting system, controlled via pulse-width modulation (PWM), is calibrated using predetermined red, green, blue and yellow (RGBY) reference objects, obviating the need for external sensors. Experimental findings underscore the significance of color choice in detection accuracy, highlighting yellow as the optimal color requiring minimal illumination. Successful object detection based on color is demonstrated at an illuminance level of approximately 50 lx, accompanied by energy savings contingent upon ambient lighting conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications

Akış,

Dişlitaş

2024

Applied Sciences

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“…The first module for detecting a defect of O-rings and the second one for measuring its dimension and thickness [44]. In [57], a plane for automatic illusion regarding inspection system for robot vision was proposed. In that system, through using of intensity histogram, features were extracted and for amplifying the difference in primary features, a submodule using a neural network was deployed before sending it to the classifier.…”

Section: Introductionmentioning

confidence: 99%

“…In that system, through using of intensity histogram, features were extracted and for amplifying the difference in primary features, a submodule using a neural network was deployed before sending it to the classifier. Then, they applied the stacked auto-encoder as an amplifier to get the evolved features just because those are more discriminative [57]. Afterward, assessment of brightness quality of the image was interpreted to the problem of classification.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

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