# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 4 Experiment and Results
### Experimental Results
#### 4.2.4 Improved Experimental Results Based on GIOU Loss  
In this section, we will verify the effects of replacing the original L2 loss with GIOU loss in the position regression loss. The improved algorithm 1 in this section requires the following experimental conditions: the anchor for clustering of the vehicle detection scenario described in this paper, multi-scale training, not using label smoothing, and using mixup for the first 10 epochs of training. The loss function is as follows: using the GIOU loss as position loss, using the L2 norm loss function as confidence loss, and using cross entropy as class loss. The improved algorithm 2 in this section requires the following experimental conditions: the anchor for clustering of the vehicle detection scenario described in this paper, multi-scale training, using label smoothing, and using mixup for the first 10 epochs of training. The loss function is as follows: using the GIOU loss as position loss, using the L2 norm loss function as confidence loss, and using cross entropy as class loss.  
As shown in Table 6 and Figure 13, the two improved algorithms have very similar performance. The average precision of improved algorithm 2 is 0.22% higher than that of improved algorithm 1. They also have very close performance in precision for each class, and the difference is that the improved algorithm 1 does not use label smoothing, while the improved algorithm 2 uses it. According to the  
Figure 11: Comparison of Faster-RCNN, original YOLO and improved YOLO algorithm with IoU loss function in terms of precision.  
Figure 12: Comparison of Faster-RCNN, original YOLO and improved YOLO algorithm with IoU loss function in terms of omission ratio.  
experimental results in this section, it can be seen that GIOU can also be used as the position regression loss function to well optimize the model.  
As shown in Figure 14, similar to the comparison in terms of precision, the omission ratio of improved algorithms in this section is significantly lower than that of the Faster-RCNN algorithm and the original YOLO algorithm.  
To sum it up, we believe that GIOU and IoU can be used as the position loss function to well regress the target position, accelerate the model's convergence speed, reduce the training volume of the model and improve the final performance of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Faster-RCNN** & **Original YOLO** & **Improved YOLO with IoU** & **Improved YOLO with GIOU** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Suv & 75.10 & 71.80 & 89.63 & 90.30 \\ Sedan & 74.78 & 88.55 & 89.97 & 90.34 \\ Taxi & 85.83 & 89.55 & 89.91 & 89.99 \\ Van & 85.50 & 82.15 & 93.03 & 92.53 \\ Truck-Box & 89.95 & 76.94 & 95.82 & 95.82 \\ Bus & 90.01 & 82.94 & 96.04 & 96.36 \\ MiniVan & 77.27 & 74.48 & 89.56 & 89.83 \\ Truck-Util & 95.53 & 40.82 & 95.36 & 95.4 \\ Pickup & 90.89 & 35.54 & 92.99 & 93.74 \\ mAP & 84.99 & 71.42 & 92.48 & 92.70 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Faster-RCNN, original YOLO and improved YOLO algorithm with GIOU loss function in terms of precision.  
Figure 13: Comparison of Faster-RCNN, original YOLO and improved YOLO algorithm with GIoU loss function in terms of precision.