Real Time Video Analysis From Conveyor Belt

Abstract

This reseaech introduces a real-time video analysis system for industrial conveyor belt monitoring that uses YOLOv8 for object detection. YOLOv8 effortlessly detects and tracks a wide range of objects on the moving belt, enabling pinpoint accuracy in its detection. With Tesseract, the system can now extract text from detected objects, allowing for the collection of textual information. Integrating with MQTT makes it even easier for distributed devices to communicate and share data, which speeds up decision-making based on that data.An Internet of Things (IoT) edge device is a crucial component of this system upgrade; it creates a live connection to data collected from sensors and cameras installed throughout the industrial setting. We are able to conduct continuous monitoring and analysis because our code effortlessly takes this live input. The integration of YOLOv8, Tesseract, MQTT, and the IoT Edge device demonstrates significant advancements in industrial conveyor belt operations’ automation and quality control. shown by this system’s utilization of YOLOv8, Tesseract, and the Internet of Things Edge device