Integrating Computer Vision and Deep Learning for Automated Object Diagnosis and Repair

The rapid advancement of AI, particularly in computer vision and deep learning, has revolutionized industrial automation. This paper presents an AI-driven system that integrates computer vision and deep learning for real-time object diagnosis and repair. Using models like YOLO, Faster R-CNN, and SSD, the system captures images or video feeds, detects defects, and either recommends or initiates repairs through predefined rules or robotic systems. Convolutional Neural Networks (CNNs) classify defects such as scratches, cracks, or missing parts, reducing reliance on manual inspections, which are often slow and error-prone. By automating defect detection and repair, the system enhances efficiency, minimizes errors, and reduces downtime in industries such as manufacturing, automotive, electronics, and healthcare. Real-time analysis enables immediate corrective actions, improving productivity and cost-effectiveness. Designed as an independent AI diagnosis tool or part of a robotic automation system, it streamlines defect detection and repair with high precision and reliability. Beyond defect identification, it assesses damage severity and provides context-aware repair recommendations. In automated settings, the system can autonomously trigger repair actions. Reinforcement learning further optimizes repair strategies over time. An intuitive user interface allows operators to monitor defects, view severity levels, and access repair recommendations. Stored inspection data supports predictive maintenance, reducing unexpected failures. By continuously improving its performance through machine learning, this scalable solution enhances maintenance workflows, ensuring higher equipment uptime and reduced manual intervention.