Enhancing Industrial Automation: Real-Time Detection of Plastic Spoon Presence in Cup Filling Using Deep Learning and Plc Integration - Kanoksak Chawlert; Anuntapat Anuntachai (My University, 2025)
This paper presents a real-time detection system to enhance industrial automation in cup-filling processes by identifying the presence of plastic spoons using Deep Learning and Programmable Logic Controller (PLC) integration. The proposed system employs the YOLO (You Only Look Once) object detection model to process images captured by a vision camera. The model runs on a PC and communicates with a BECKHOFF PLC to perform automated decisionmaking based on detection results. A hardware simulation environment was developed, consisting of a servo-driven rotary table and camera setup, mimicking an actual production line. The system detects two classes: cups with spoons (acceptable) and cups without spoons (defective). Upon detection, the result is sent to the PLC, which initiates appropriate control actions. Experimental testing demonstrates that the proposed AI-integrated automation system improves detection accuracy, reduces errors from conventional sensors, and ensures better product quality control in real-time operations.
Keywords: Industrial Automation and Control Systems; Industrial Vision Systems and Defect Detection; Food Supply Chain Traceability