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

Abstract

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.