# Occlusion Vehicle Target Recognition Method Based on Component Model
## 3 Experiment and Result Analysis
### Comparative Experiments  
To verify the advanced characteristics of the VSRS module backbone network designed in Section 2.1 of this paper, the backbone network of this module is compared with the currently realized and representative mainstream instance segmentation models, including DetNet, which does not use pre-trained classifiers, and vgg16 and resNet101, which use pre-trained classifiers as backbone networks, respectively. The parameters and environment configured during the experiment were consistent. The performance pairs are shown in Table 3.  
As can be seen from Table 3, mAP, mIoU, accuracy, precision, and Recall of the backbone network using the proposed method are 4.83%, 4.62%, 5.72%, 5.6% and 4.15% higher than that of vgg16, respectively. They are 2.96%, 1.12%, 2.38%, 3.98% and 1.49%  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component Perception** & **Efficient Attention** & \multicolumn{3}{c}{**PASCAL VOC2012**} \\ \cline{2-5}
**Enhancement Unit** & **Unit** & **Precision/\%** & **Accuracy/\%** & **mAP/\%** \\ \hline \(\times\) & \(\times\) & 87.9 & 82.8 & 88.8 \\ \(\surd\) & \(\times\) & 89.5 & 81.8 & 91.1 \\ \(\times\) & \(\surd\) & 88.1 & 86.0 & 92.5 \\ \(\surd\) & \(\surd\) & 90.8 & 88.7 & 92.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experimental results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **mAP/\%** & **mIoU/\%** & **Accuracy/\%** & **Precision/\%** & **Recall/\%** \\ \hline Vgg16 & 79.57 & 82.19 & 80.4 & 81.09 & 80.01 \\ ResNet101 & 81.44 & 85.69 & 83.74 & 82.71 & 82.67 \\ DetNet & 83.29 & 84.58 & 84.25 & 84.91 & 82.91 \\ The method proposed in this paper & 84.4 & 86.81 & 86.12 & 86.69 & 84.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results of VSRS module backbone performance comparison.  
higher than ResNet101. It is 1.13%, 2.23%, 1.87%, 1.78% and 1.25% higher than DetNet, respectively. It can be concluded that the performance of vgg16 is the lowest of all the above algorithms. ResNet101 algorithm and DetNet algorithm have better performance. The method module in this paper is superior to the above four algorithms in recognition accuracy and segmentation accuracy, and shows better performance.  
In order to verify the advanced nature of the vehicle component feature extraction classification module designed in Section 2.2, the module is compared with the currently implemented and representative mainstream instance segmentation modules, including SOLOv2 [25], Mask R-CNN [26], Cascade Mask R-CNN [27], QueryInst [28]. The parameters and environment configured during the experiment are consistent. The performance comparison is shown in Table 4.  
It can be seen from Table 4 that the mAP, mIoU, accuracy, Precision, and Recall of the proposed method are 4.52%, 5.61%, 6.42%, 5.52%, and 5.15% higher than SOLOv2, respectively, and 5.43%, 3.19%, 2.95%, 3.53%, and 3.4% higher than the CNN-based vehicle segmentation algorithm Mask R-CNN method, respectively. In addition, compared with the detection accuracy and segmentation accuracy of other advanced modules, it can be concluded that the overall detection accuracy and segmentation accuracy of SOLOv2 are the lowest among all the above algorithms. The Cascade Mask R-CNN algorithm and QueryInst algorithm have relatively good detection accuracy and segmentation accuracy. The module proposed exceeds the above four algorithms in terms of recognition accuracy and segmentation accuracy, showing better performance. Therefore, the proposed method can better extract and utilize the features of the target, and the module segmentation performance is better.