## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
From Figures 10 and 11, it can be seen that the frequency domain SR saliency detection method can highlight high-frequency information, but at the same time preserve non-target areas such as leaves. By using structural edges to constrain and filter out non structural regions in the spatial domain, a series of candidate boxes is obtained. The most likely structural regions are 15 boxes, each represented by different colors, as shown in Figures 10c and 11c. Finally, the vehicle target is detected, as shown in Figures 10d and 11d. The vehicle target is set to the target and the surrounding environment is set to the background. The GrabCut segmentation algorithm and the maximum inter class variance threshold segmentation method are combined to segment the target and background of the detected vehicle target area. The vehicle targets in different scenes are segmented

Figure 11: Test results of incomplete vehicle image. (**a**) Original image of incomplete vehicle image. (**b**) SR algorithm detection results. (**c**) Structural area detection results. (**d**) Final detection results. Figure 10: Test results of complete vehicle image. (**a**) Original image of complete vehicle image. (**b**) SR algorithm detection results. (**c**) Structural area detection results. (**d**) Final detection results. separately. The segmentation results of complete vehicle image are shown in Figure 12, and the segmentation results of incomplete vehicle image are shown in Figure 4:0

After achieving target detection and segmentation of visible light images, they are fused with infrared images to reduce the probability of false detections and missed detections. The infrared images are converted into grayscale images, and the vehicle targets in the infrared images are set to strong radiation and the surrounding buildings are set to medium radiation for different targets and environments. A 3*3 operational filter is selected to maximize the preservation of infrared image details. The feature extraction results of the complete vehicle image are shown in Figure 14, and the feature extraction results of the incomplete vehicle image are shown in Figure 4:1

Figure 4:2 Comparison of segmentation results for complete vehicle image. (**a**) Complete vehicle image. (**b**) GrabCut algorithm. (**c**) Maximum inter class variance algorithm. (**d**) The method proposed in this paper. (**e**) Extracting targets. Figure 4:3 Comparison of segmentation results for incomplete vehicle image. (**a**) Incomplete vehicle image. (**b**) GrabCut algorithm. (**c**) Maximum inter class variance algorithm. (**d**) The method proposed in this paper. (**e**) Extracting targets. The visible light image and infrared image are fused based on IOU judgment; the fusion result is shown in Figure 16a for a complete vehicle image, and the fusion result is shown in Figure 16b for an incomplete vehicle image. In these figures, the green box represents the infrared image detection results, and the blue box represents the visible light image detection results. Figure 4:4 Feature extraction results of complete vehicle image. (**a**) Infrared image of complete vehicle image. (**b**) Infrared image containing Gaussian noise. (**c**) 3 \(\times\) 3 filter. (**d**) Infrared image segmentation. (**e**) Box selection of infrared target. (**f**) Extraction of infrared target. Figure 4:5 Feature extraction results of incomplete vehicle image. (**a**) Infrared image of incomplete vehicle image. (**b**) Infrared image containing Gaussian noise. (**c**) 3 \(\times\) 3 filter. (**d**) Infrared image segmentation. (**e**) Box selection of infrared target. (**f**) Extraction of infrared target. Using naive Bayesian inference to calculate the target recognition results in both complete and incomplete vehicle images.