Enhancing Object Sorting Under Low-Light Conditions with CLAHE, Gaussian Blur, ROI, and Custom PID on a Raspberry Pi Robotic Arm
Jie Ying Wu · Applied and Computational Engineering · 2024
This paper addresses the significant challenge faced by robotic vision systems in detecting and sorting objects accurately under varying lighting conditions. Such variations in light can lead to decreased detection accuracy and inefficiencies in automated sorting processes. The paper employs a combination of literature review and experimental validation to investigate the effectiveness of advanced image processing techniques and control algorithms. Specifically, it explores the application of CLAHE adaptive compensation, Gaussian Blur, custom ROI, and PID controllers within a visual object sorting system to improve its robustness under diverse lighting conditions. The use of CLAHE and Gaussian Blur effectively compensates for uneven lighting, while custom ROI and PID controllers further optimize the system's response to fluctuating conditions.