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.

中文翻译：

---

基于现代 YOLO 深度学习算法的鲁棒多类 3D 对象识别

多类 3D 对象识别在准确性和速度方面都经历了许多演变。本研究介绍了用于多类 3D 对象检测和识别的现代 YOLO 算法（YOLOv3、YOLOv4 和 YOLOv5）的实现。所有YOLO算法都根据一个非常大的规模数据集（Pascal VOC数据集）进行了测试。绩效评估针对以下指标的计算；mAP（平均精度）、召回率、F1-score、IOU（交于并集）和运行时间。实验结果表明，YOLOv3 的目标 mAP 为 77%，IOU 为 0.41，总运行时间接近 8 小时。此外，在 YOLOv4 中，它的目标 mAP 为 55%，IOU 为 0.035，总运行时间接近 7 小时。此外，YOLOv5建立了48%的mAP，0.045的IOU，总运行时间约为3 h。最后，在优化其超参数和分层系统的最新技术中，已经提出了 YOLOv5 的修改版本。因此，在运行 3 小时时，mAP 得分约为 55%。本研究的最终结论表明，YOLOv3 的识别准确率最高，然而，提出的修改后的 YOLOv5 的处理时间最短。