[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-134675-en":3,"doc-seo-134675-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},134675,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications - Article","Applications reliant on image processing require lighting management that supports accurate object detection while maintaining efficient energy use. Conventional lighting control typically relies on manual switching, timed activation, or sensor-driven adjustments from illuminance readings. This study presents an embedded approach to intelligent ambient lighting using reference-color image processing for object detection and positioning in robotic handling. Performance is assessed through illuminance and power-consumption measurements in a tailored setup, using PWM-controlled RGBY reference calibration without external sensors.","applied sciences  \nArticle  \nIntelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications  \nU ˘gur Akı¸s 1 and Serkan Di¸slita¸s 2, *  \nCitation: Akı¸s, U.; Di¸slita¸s, S. Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications. Appl. Sci. 2024, 14, 3002 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app14073002  \nAcademic Editors: Daynier Rolando Delgado Sobrino and Rafał Goł˛ebski  \nReceived: 28 February 2024  \nRevised: 24 March 2024  \nAccepted: 1 April 2024  \nPublished: 3 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Energy Systems Engineering, Hitit University, 19030 Çorum, Türkiye  \n2 Department of Computer Engineering, Hitit University, 19030 Çorum, Türkiye  \n* Correspondence: [serkandislitas@hitit.edu.tr](serkandislitas@hitit.edu.tr)  \nAbstract: 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.  \nKeywords: intelligent lighting; image processing; color-based object detection; robotics  \n1. Introduction  \nThe efficient use and saving of electrical energy are as important as its generation [1] . Natural and artificial light sources are used in lighting, one of the most important usage areas of electrical energy. Daylighting is a quality light source and has the best color rendering ability. In order to save energy, it is necessary to make maximum use of daylight and to use intelligent lighting systems in this context [2] . There are intelligent lighting systems with commercial, energy-efficient and advanced features for sectoral needs [3] . Commercial intelligent lighting systems are systems with functional features, such as on/off, dimming, monitoring and programmability. Energy-saving intelligent lighting systems are systems, which consume less electrical energy, usually by working with the help of some sensors within an algorithm and software. In advanced intelligent lighting systems, in addition to energy saving, light quality control can be achieved by adjusting features such as light intensity, direction, angle and distance with various algorithms and artificial-intelligence-based applications [4–9] .  \nImage-processing-based robotic systems are widely used in industrial areas for fast and accurate detection, tracking and classification of objects for handling operations. With a general definition, image processing is a technology, which enables ","cbCaioQ3wWHKC2ml","https://ap.wps.com/l/cbCaioQ3wWHKC2ml","pdf",9058965,1,15,"English","en",105,"# Introduction\n## Background and motivation\n## Image-processing-based robotic detection\n## Impact of ambient lighting on color-based detection\n## LED lighting and minimum illuminance needs","[{\"question\":\"What problem does the research address in color-based robotic handling?\",\"answer\":\"The study targets the challenge of ensuring reliable color-based object detection under varying ambient lighting while also reducing energy consumption.\"},{\"question\":\"How does the proposed system control and calibrate lighting?\",\"answer\":\"It uses an embedded lighting controller with PWM control and calibrates LED illumination using predetermined RGBY reference objects, avoiding external sensors.\"},{\"question\":\"Which reference color performed best for detection accuracy?\",\"answer\":\"Experimental results indicate that yellow achieved the best detection performance while requiring the minimum illuminance (around 50 lx).\"}]","Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications - 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