## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
When the vehicle image is complete, the recognition rate of vehicle targets is about 76.8%, the recognition rate of buildings is about 0, and the recognition rate of grass is about 57.4%; when the vehicle image is incomplete, the recognition rate of vehicle targets is decreased to 73.7%, the recognition rate of buildings is still 0, and the recognition rate of grass is increased to 59.7%. The detection results of vehicle targets based on the recognition methods presented in this paper and Reference [24] are shown in Figure 4:6

Figure 4:7 Fusion results of visible light image and infrared image. (**a**) Fusion results when the vehicle image is complete. (**b**) Fusion results when the vehicle image is incomplete. When the imaging device is shooting at different distances, the change curve of the vehicle target recognition rate is shown in Figure 4:8 In Figure 17, it can be observed that as the shooting distance of optical imaging equipment increases, the recognition rates of both the method in this paper and the method in Reference [24] gradually decrease. However, the recognition results of the method in this paper are still higher than those of the method in Reference [24], indicating that the vehicle target recognition method based on visible light and infrared image fusion in this paper has stronger adaptability and better detection and recognition results. ## 6 Conclusions

This paper first performs saliency region detection on visible light images captured by optical imaging equipment. By combining SR and EdgeBox detection methods, vehicles in visible light images are detected and labeled. The GrabCut algorithm and maximum inter class variance method are combined to segment vehicle targets in the recognition box of the labeled image. Secondly, we filter and process the infrared images of vehicles captured by infrared devices to extract feature information of the vehicle target. Thirdly, based on the processed visible light image and infrared image, the authenticity of the target is judged using IOU judgment, and Bayesian inference fusion processing is used to obtain the final vehicle target recognition result. Finally, the effectiveness of the proposed method was verified through experimental results and comparative analysis. Conceptualization and methodology and validation, writing--original draft, J.W. ; methodology, software and validation, writing--original draft, X.Z. All authors will be informed of each step of the manuscript processing, including submission, revision, revision reminder, etc., via emails from our system or assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 62073256. Not applicable. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **The Vehicle Image** & **The Vehicle Image** \\  & **Is Complete** & **Is Incomplete** \\ \hline Recognition probability in this paper & 0.77 & 0.74 \\ \hline Recognition probability of Reference [24] & 0.68 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition probability of vehicle target. Figure 17: The change curve of vehicle target recognition rate at different distances. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflict of interest. ## References

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