3D VISION OBJECT IDENTIFICATION USING YOLOv8

This article aims to explore the application of 3D vision techniques in the automation of logistical processes, using deep learning for object identification and manipulation in industrial environments.The work develops a 3D vision system that employs object and keypoint detection models trained with tools such as Roboflow and YOLOv8 (You Only Look Once version 8).The methodology includes data collection and annotation, development of deep learning models, and analysis of the obtained results.The models demonstrated high precision and recall in the block and keypoint identification, with a slight reduction in keypoint model accuracy due to variability in annotations.Integrating the models presented computational challenges, but the combined approach proved effective in precise detection.Limitations include the need for resource optimization and improvement in annotation processes.In addition, the accuracy obtained is due to the fact that the object detection system was trained with large amounts of data to provide this high accuracy.The model was trained and evaluated based on measures of average precision (mAP) and recovery, obtaining a mAP of 98.3%, a precision of 96.4% and a recall of 95.6%.