(2025-01-28) Santos, Williams da Conceição dos; Fialho, Michaella Socorro Bruce; Farias, Nei Junior da Silva
This study addresses the integration of YOLOv8 and ArUco Markers to improve screw detection
and measurement. The main objective is to develop a solution that will allow the identification and
measurement of screws accurately using computer vision. The methodology involves using a webcam to capture images, where the YOLO algorithm performs object detection, and ArUco Markers are used to
calculate screw dimensions. The system was implemented to capture and save images of objects, in addition to recording measurements in a spreadsheet. The research was carried out in a controlled environment, focusing on systems integration. The results improved the system's efficiency in identifying and measuring screws in an automated and precise way, showing that the combination of computer vision techniques can increase the capacity of robotic systems in object manipulation and inspection tasks, being viable in industrial inspection processes. and quality control.
