## 5 Experimental Results and Analysis
### Experiments of Object Detection Algorithm  
#### 5.3.1 Comparison and Analysis of Model Evaluation Indicators  
The data set VisDrone2019 is used in this experiment. The data set consisted of 10,209 static images and 11 types of object. The VisDrone dataset was captured using drone cameras and data were collected using different drone platforms in different scenarios with different weather and lighting conditions, with high-definition, diverse scenes, and rich target tagging. In this paper, only vehicle objects were detected, so pictures of four object types, namely, car, bus, truck and van, were extracted for training.  
After processing the data set into the directory structure required by YOLOv5s, the YOLOv5s configuration file was modified, the number of iterations was set to 100, and the output YOLOv5s and improved YOLOv5s network model were trained, as well as corresponding indicators and indicator graphs. The indexes of recall rate and the PR curve (mAP) value of the model were analyzed to judge the quality and availability of the model. Finally, YOLOv5s and the improved YOLOv5s network model were used to detect the vehicles in the video, and the detection effect was judged.  
The recall rate and mAP of the original YOLOv5s and the improved YOLOv5s model trained on the VisDrone2019 vehicle detection data set are shown in Figures 10 and 11, respectively. According to the experiment results, we can see that the addition of the AIFI module significantly improved the detection accuracy of the improved YOLOv5s model. Compared to the original YOLOv5s model, the recall rate increased by 7.7%, while the mean average accuracy (mAP) for cars, buses, trucks, and vans improved by 6.3%, 18.6%, 29.2%, and 11%, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware Configuration** & **Version** \\ \hline CPU & Inter(R) Core(TM) 12700 KF 3.60 GHz, Intel, Shanghai, China \\ Memory & 16 Gb \\ GPU & NVIDIA GeForce 3080 RTX, NVIDIA, Shenzhen, China \\ Operating System & Windows10, 64 Bit \\ \hline \end{tabular}
\end{table}
Table 4: Experimental hardware environment.  
\begin{table}
\begin{tabular}{c c} \hline
**Software Configuration** & **Version** \\ \hline Language & Python 3.8.5 \\ Deep Learning framework & Pytorch 1.8 \\ GPU Engine & Cuda 12.1 and cundann 7.6 \\ Virtual Environment & Miniconda3 \\ \hline \end{tabular}
\end{table}
Table 5: Experimental software environment.  
YOLOv8 introduced a new State of the Art (SOTA) model, including P6640 and P61280 resolution target detection networks and the Yolact-based instance segmentation model. Therefore, in order to obtain a more objective evaluation of the algorithm presented in this paper, we have proposed a novel method for segmentation of the object detection model.  
Figure 11: PR curve performance of object detection model: (**a**) car; (**b**) bus; (**c**) truck; (**d**) van.  
Figure 10: Recall rate of object detection models.  
paper, the performance in terms of the number of parameters, the computational capacity (FLOPs), and the average precision of the improved YOLOv5s is compared with the original YOLOv5s and YOLOv8s models.  
The VisDrone data set was used to extract car, bus, truck, and van information for training. A total of 6192 pictures were collected in the training set and 519 pictures were collected in the verification set. The results are shown in Table 6. Compared with the original YOLOv5s, the number of parameters of the improved YOLOv5s presented in this paper increased by 2.7%, FLOPs increased the same amount, and average precision \(\text{AP}^{val}_{50.95}\) and \(\text{AP}^{val}_{50}\) increased by 16.2% and 15.5%, respectively. Compared to YOLOv8s, the number of parameters and FLOPS of the improved YOLOv5s decreased by 33.93% and 42.3%, while \(\text{AP}^{val}_{50.95}\) and \(\text{AP}^{val}_{50}\) increased by 7.5% and 16.5%, respectively. This means that, compared with the YOLOv8s or SOTA models, the improved YOLOv5s presented in this paper is more suitable for vehicle detection.