# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 4 Experiment and Results
### Experimental Results
#### 4.2.2 Improved Experimental Results Based on Improving Model's Generalization Ability  
In the experiments of this section, the classic two-stage target detection algorithm Faster-RCNN is used as the algorithm for comparison with the original YOLO algorithm and the improved YOLO algorithm proposed in this section. Among them, the Faster-RCNN algorithm adopts ResNet50 with stronger feature extraction ability as its backbone network. The improved algorithm 1 in this section requires the following experimental conditions: the anchor of the original YOLO algorithm, multi-scale training, using label smoothing, and using mixup for the first 10 epochs of training. The improved algorithm 2 in this section requires the following experimental conditions: the anchor for clustering of vehicle detection scenario in this paper, multi-scale training, using label smoothing, and using mixup for the first 10 epochs of training. According to Table 3 and Figure 9, it can be seen that the various precisions of the two-stage Faster-RCNN algorithm are higher than the precisions of the YOLO algorithm. This is because Faster-RCNN adopts the RPN network to generate candidate area that may contain the target, so that more candidate areas can be obtained, and the reliability is also higher. Therefore, the Faster-RCNN algorithm has low omission ratio, and its precision is improved as a result. In addition, because dataset UA-DETRAC-LITE-NEW has labeled many pick-ups captured by camera from a long distance, where it is almost impossible to see the back of pick-up with human eyes, it is difficult to detect the class of the pick-up. By comparing the performances of the Faster-RCNN algorithm, the original YOLO algorithm, the improved YOLO algorithm 1 and the improved YOLO algorithm 2 in this section, it can be seen that the mixup and label smoothing methods can be used to effectively improve the model's generalization ability, almost without increasing the network computation overhead, so as to further improve the model's accuracy. Figure 9: Comparison of Faster-RCNN, YOLO and improved YOLO in terms of precision. According to Figure 10, it can be seen that the improved YOLO algorithms have lower omission ratios. Although the improved algorithm 2 has a higher omission ratio than the improved algorithm 1 in general, similar to the situation in the previous section, because the improved algorithm involves a higher FP ratio, the improved algorithm 2 has a higher average precision than the improved algorithm 1. Furthermore, the average omission ratio of the two improved algorithms is lower than that of the Faster-RCNN algorithm for comparison, and their omission ratio for most classes is lower than that of Faster-RCNN. Their omission ratio is higher than that of Faster-RCNN only for the two classes of pick-ups and trucks, which once more proves the effectiveness of the improved algorithm. Table 4 shows the improved YOLO confusion matrix in this section. According to analysis of the confusion matrix, the probability of the model misclassifying the detected target into another class is very small. For the class of small commercial vehicles, the model falsely predicts 3.58% of small commercial vehicles as SUVs, and this is probably because the small commercial vehicles are close to SUV in height and size. In contrast, the model also falsely predicts 0.8% of SUVs as small commercial vehicles. The main difference between improved algorithm 1 and improved algorithm 2 in this section is whether the anchor for clustering of the vehicle detection scenario proposed in this paper is used. It can be seen that when the anchor obtained through clustering is used to train the model, the performance can be improved by 1.98% compared to the performance of the improved algorithm using the original anchor. Once again, it proves that the model performance can be effectively improved for scenario clustering.