Research Information System
17th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2025, Targoviste, Romania, 26 - 27 June 2025, (Full Text)
In the early 21st century, the adoption and use of robots and machines in the industrial field has increased significantly due to rapidly developing technology. Therefore, critical safety issues have arisen, especially in areas where people and machines work nearby. This research aims to develop an artificial intelligence model that detects people and estimates their distances to machines using computer vision as a solution to the problem mentioned above. The system, obtained by combining the YOLOv8 deep learning model used for object detection with distance calculation algorithms, ensures safety by continuously examining human-machine interactions. This artificial intelligence-supported detection system can be used in industrial environments such as factories and warehouses to prevent accidents and ensure safety. The system receives a video captured by monocular cameras integrated into the environment where people and machines are located as input. Later, processes each frame for human and machine detection, and places the detected people and machines in a bounding box. Then, the distance between the machines and humans is estimated by using the bounding box coordinates. The system provides feedback based on the estimate obtained, allowing immediate intervention in case a person gets too close to the machine. During the development process of the approach, different object detection models and distance measurement methods were tried. For the object detection component, different versions of YOLO from YOLOv8 to YOLO12 were trained with two different datasets, and YOLOv81 produced the best results with the mAP50 (Mean Average Precision) value of 0.948. The measuring distance using depth image, a top-down view, and meter/pixel ratio were tried for the distance measurement, and meter-pixel ratio method generated the best results. Therefore, used in the system along with the YOLOv81.