# Occlusion Vehicle Target Recognition Method Based on Component Model
## 3 Experiment and Result Analysis
### Comparison Experiment of Occlusion Vehicle Recognition Net Results  
To evaluate the actual recognition effect of VSRS-VCFM and the currently realized representative mainstream recognition algorithms, a comparative experiment was designed, and the vehicle image was divided into four types according to its occlusion forms: no occlusion, field of view occlusion, non-target occlusion, and inter-target occlusion, and prove the advantages of VSRS-VCFM. The experimental results are shown in Table 5.  
It can be seen from Table 5 that the Mask R-CNN on traffic vehicle scenes performance is the worst. The mAP, mIoU, accuracy, Precision, and Recall of the proposed method are 3.28%, 0.92%,2.81%, 2.58%, and 1.06% higher than QueryInst, respectively, and 3.19%, 1.68%, 3.46%, 1.45%, and1.95% higher than the Cascade Mask R-CNN method, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **mAP/\%** & **mIoU/\%** & **Accuracy/\%** & **Precision/\%** & **Recall/\%** \\ \hline SOLOv2 & 84.2 & 79.98 & 83.40 & 83.95 & 84.01 \\ Mask R-CNN & 83.29 & 82.40 & 86.87 & 85.94 & 85.76 \\ QueryInst & 89.4 & 84.58 & 88.65 & 88.29 & 87.95 \\ Cascade Mask R-CNN & 86.71 & 83.52 & 88.17 & 88.04 & 87.62 \\ The method proposed by this paper & 88.72 & 85.59 & 89.82 & 89.47 & 89.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results of VCFM module performance comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **mAP/\%** & **mIoU/\%** & **Accuracy/\%** & **Precision/\%** & **Recall/\%** \\ \hline Mask R-CNN & 70.41 & 70.54 & 73.06 & 72.34 & 70.14 \\ QueryInst & 70.99 & 71.78 & 73.56 & 72.12 & 73.99 \\ Cascade Mask R-CNN & 71.08 & 71.02 & 72.91 & 73.47 & 73.1 \\ The method proposed in this paper & 74.27 & 72.7 & 76.37 & 74.92 & 75.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of performance comparison of VSRS-VCFM in recognizing traffic vehicle scenes on UA-DETRAC. The superiority of this method is proved. In summary, the module proposed in this paper exceeds the above three algorithms in recognition accuracy and segmentation accuracy, showing better performance. Therefore, the method can better identify the vehicle parts, so as to realize the identification of shielding vehicles. A set of representative vehicle target images in different states are selected. The algorithm is compared with the representative mainstream occlusion target recognition algorithms Cascade R-CNN algorithm and QueryInst algorithm. The results are shown in Figure 9. Figure 9a shows the actual images of occluded vehicles. The detection and segmentation results of the Cascade R-CNN are shown in Figure 9b, the detection and segmentation results of the QueryInst model are shown in Figure 9c, and the detection and segmentation results of the proposed algorithm are shown in Figure 9d. The test results are shown in Figure 9. The missed part is marked with a green box and the over-segmentation part is marked in red boxes. In the first set of images, the Cascade R-CNN model failed to detect some components and incorrectly classified some obstacles as components, resulting in missed detection and false detection. The Querqvist model does not detect some components, and the effect of vehicle contour detection is poor. Due to the introduction of the VSRS module and VCFM module, the model can segment the vehicle more accurately and can correctly identify the missing areas in the previous two models. In the second set of images, the Cascade R-CNN model has a poor detection effect on vehicle components. Querqvist also has a poor detection effect on the vehicle contour. In the model testing results of this paper, the accurate segmentation of vehicle parts and vehicles can be realized. In the third group of images, the Cascade R-CNN model appears to recognize vehicle leakage, and the detection effect of the vehicle contour is poor. [PERSON] also has some cases of missing recognition.