## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm  
As the imaging system is shaken by external influences during the shooting process, resulting in the degradation of the quality of the captured images, the loss of information, and the formation of blurry pictures, there is a negative impact on the accuracy of target recognition. The Wiener filter method is used to restore the fuzzy image, and the method model is Formula (1), where \(H(u,v)\) is the degradation function and \(G(u,v)\) is the Fourier transform of the degenerated image. \[\hat{F}(u,v)=\frac{G(u,v)}{H(u,v)}\frac{|H(u,v)|^{2}}{(|H(u,v)|_{2}+K)} \tag{1}\]  
In Formula (1), \(H(u,v)=\frac{T}{\pi(uu+vb)}\sin[\pi(ua+vb)]e^{j\pi(uu+vb)}\), \(G(u,v)=F(u,v)H(u,v)\). The method for recognizing visible light images usually starts from the upper left corner of the image and traverses the entire image to search for the target, which not only takes time but also increases computational complexity. As a result, saliency region detection, which is used to suspect the real target, is performed before target recognition. That is, some information that does not have basic target features is filtered out from the salient regions, and the structural edge features of the vehicle target are used to constrain and improve the accuracy of real target recognition. Saliency region detection methods are divided into spatial domain detection methods and frequency domain detection methods. Among these, frequency domain detection methods are prone to losing target edge information, and although the detection method based on the spatial domain can retain the details of image objects, it is difficult to remove interference factors such as ground shadows. Based on the advantages and disadvantages of two detection methods, this paper combines the spatial domain detection method and the frequency domain detection method to detect suspected vehicle target areas. The SR algorithm based on the frequency domain detection method is chosen in which the visible light image is transformed to the frequency domain and the low-frequency components are suppressed by using a high-pass filter to highlight high-frequency information such as target edge contours. The specific steps of SR algorithm (Algorithm 1) are as follows:  
```
Step 1: Visible light image \(g(x,y)\) is transformed into frequency domain space \(g^{\prime}(u,v)\) through Fourier transform. The amplitude spectrum \(A(u,v)\), phase spectrum \(X(u,v)\), and logarithmic amplitude Step 2: spectrum \(L(u,v)\) in the frequency domain are calculated, which are \(A(u,v)=|F(g)|\), \(X(u,v)=angleF(g)\), \(L(u,v)=\log(A(u,v))\). Step 3: The spectral residual \(\mathcal{C}(u,v)\) is calculated, which is \(C(u,v)=L(u,v)-h_{n}\cdot L(u,v)\). Step 4: The significance map is obtained, which is \(S(x,y)=\Big{|}F^{-1}[\exp(C(u,v)+i\cdot X(u,v))]^{2}\Big{|}\). ```  
**Algorithm 1** The SR Algorithm  
Based on the spatial domain detection method, the EdgeBox algorithm is selected. Its basic idea is to perform structural edge detection in visible light images, score all candidate regions based on the detection results, and output structurally significant regions through scores [26]. In the process of image detection, the detection boxes on the same target are closer or even overlapping, while the detection boxes on different targets are farther apart. This paper designs an iterative automatic clustering algorithm for target boxes. The specific steps of the SR EdgeBox algorithm (Algorithm 2) are as follows:  
```
Step 1: \(n\) candidate boxes \"\(box\)\" are entered. All \(box\) in the queue is placed, the first candidate box \(box_{1}\) is selected as the benchmark, and calculated in sequence whether there is overlap with the other \(n-1\ box\). If two candidate boxes overlap, the minimum rectangle \(box_{\min}\) is calculated that surrounds the two candidate boxes, candidate box \(box_{1}\) is replaced.