A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

A multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F1-score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state-of-the-art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.