Color and Defect Detection in Industry Automation for Real Time Intelligent Product Sorting

This paper presents a real-time object sorting system that classifies objects based on color and detects surface defects in an intelligent manner for multi-purpose application like autonomous vehicles, supply chain, robotics and so on. The proposed system uses state-of-the-art camera module to capture images of objects and processes them through computer vision to classify items based on color and defect status. The captured images are compared against a custom dataset of labeled samples to ensure accuracy. Based on performance metrics, the intelligent CNN algorithm demonstrates superior classification accuracy compared to RFC, especially in complex scenarios. The edge processor module controls sorting mechanism, where servos actuate to direct objects into designated bins based on classification results. The research work illustrates exploring the potential of machine learning algorithm in automated quality assurance, offering scalable, adaptable, and efficient solutions for industrial applications.