## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
\[P(f|s)=\frac{P(s|f)P(f)}{P(s)}\propto P(s|f)P(f)=P(car|f)P(building|f)P(grass|f)P(f) \tag{4}\]

In Formula (4), the posterior probability function \(P(f|s)\) represents the probability that event \(f\) occurs after event \(s\) occurs, the likelihood function \(P(f|s)\) represents the probability of event \(s\) occurring when event \(f\) is true, the prior probability function \(P(f)\) represents the probability that event \(f\) will occur before event \(s\) occurs, which is determined by the specific scene and the accuracy of the detection algorithm [30], and the marginal likelihood function \(P(s)\) is not equal to \(0\) and is independent of any assumptions. By comparing the magnitude of \(P(f=1|s)\) and \(P(f=0|s)\), it is deduced whether the captured image contains a vehicle target; if the former is large, it is considered that the vehicle target has been detected, otherwise it is considered that the vehicle target has not been detected. The probability of detecting a vehicle is shown in Formula (5). \[ \begin{split}& P(f=1|s)=\frac{P(s|f=1)P(f=1)}{P(s)}\propto P(s|f=1)P(f=1) =\\ & P(car|f=1)P(building|f=1)P(grass|f=1)P(f=1)\end{split}  \tag{5}\]

## 5 Experimental Analysis

During the experiment, if the environmental impact causes shaking during the shooting process of the imaging system, causing the image to be blurred, the image is restored first and then detected. The specific process is that the SR algorithm and the EdgeBox algorithm are applied to detect visible light images. Due to the SR algorithm's inability to preserve image target details well, the EdgeBox algorithm can preserve image target details. However, the EdgeBox algorithm cannot remove interference factors such as ground shadows. Thus, this paper combines spatial domain detection methods with frequency domain detection methods to detect suspected vehicle target areas. The results are shown in Figures 4-6. Figure 3: Naive Bayesian network structure. If the image captured by the imaging system does not have blur, there is no need to restore the image, and the test is performed directly. The results are shown in Figures 7-9. Figure 4: Detection results based on SR algorithm. (**a**) Original vehicle image. (**b**) Restored image. (**c**) Extract high frequencies based on SR algorithm. (**d**) Thresholding processing. (**e**) Divided the vehicle area Mask part. Figure 5: Detection results based on EdgeBox algorithm. (**a**) Original vehicle image. (**b**) Restored image. (**c**) Extract based on EdgeBox algorithm. (**d**) Thresholding processing. Figure 6: Detection results by combining SR and EdgeBox algorithms (image blurring). (**a**) Remove low-frequency areas. (**b**) Obtain the final target area. Comparing with detection results of Figures 6 and 9, it can be seen that the vehicle image after deblurring and the vehicle image without blurring can be divided by the SR algorithm to achieve the division of the vehicle area mask, the image target details can be retained through the EdgeBox algorithm, and we can use a method based on the combination of the spatial and frequency domains to detect suspected vehicle target areas. To verify the effectiveness of the algorithm proposed in this paper, tests were carried out when the vehicle image information is in different state conditions; the test results when the vehicle image is fully captured are shown in Figure 10, and the test results when the vehicle image is not fully captured are shown in Figure 11.

Figure 8: Detection results based on EdgeBox algorithm. (**a**) Original vehicle image. (**b**) Extract based on EdgeBox algorithm. (**c**) Thresholding processing. Figure 7: Detection results based on SR algorithm. (**a**) Original vehicle image. (**b**) Extract high frequencies based on SR algorithm. (**c**) Thresholding processing. (**d**) Divided the vehicle area Mask part. Figure 9: Detection results by combining SR and EdgeBox algorithms. (**a**) Remove low-frequency areas. (**b**) Obtain the final target area.