## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
At the same time, the current candidate box is deleted, and then continue to calculate whether there is overlap between \(box_{1}\) and the other \(box\);  The overlap result between candidate boxes \(box_{1}\) and all other \(box\) are calculated and the corresponding minimum bounding rectangles are obtained, candidate box \(box_{1}\) is \(box_{1}\), added to the last \(box\) and the original \(box_{1}\) is removed, candidate box \(box_{2}\) becomes \(box_{1}\), candidate box \(box_{3}\) becomes \(box_{2}\), and so on. Then, continue with step 2, repeating the process \(n\) times; Step 4: The final \(m\) possible target boxes \((m\leq n)\) are outputted. ```  
**Algorithm 2** The SR EdgeBox Algorithm  
In each iteration, the candidate boxes with overlapping relationships are replaced by the minimum bounding rectangle until there is no overlap area between the target boxes, so as to achieve automatic clustering. This paper combines the SR EdgeBox algorithm with the spatial domain and frequency domain. Firstly, the EdgeBox algorithm is used to extract edges in the spatial domain. Secondly, the SR algorithm is used to extract high-frequency information in the frequency domain, filter low-frequency information, and finally obtain the target area of the visible light image. By giving a box representing the vehicle target area, the background can be better removed, the target body can be segmented, and the interactive iterative graph GrabCut algorithm is used to segment the target and background on the image. This GrabCutalgorithm does not require training, as after giving it the vehicle target box, the pixels in the box are automatically regarded as possible targets and backgrounds through the algorithm, and the pixels outside the frame are regarded as the background [27]. Then, Gaussian hybrid models are established for the target and background data based on the similarity characteristics of the vehicle target pixels; the unlabeled pixels are inferred by minimizing the energy function. The energy function is expressed by Formula (2). \[E(o,p,q)=\psi(o,p,q)+\epsilon(o,q) \tag{2}\]  
In Formula (2), \(o\) represents the labels for each pixel segmentation in the image, \(p\) represents the parameters of the segmentation model, \(q\) represents the pixel values of the image, \(\psi(o,p,q)\) represents the main data items of the target image, and \(\epsilon(o,q)\) represents the smoothing terms of the similarity between pixels within the target. The advantage of the GrabCut algorithm is that it can effectively utilize the continuity between adjacent pixels for segmentation, but the algorithm requires continuous pixel labeling and performs poorly in situations when the boundaries of target and background are unclear and the contrast between them is small. The threshold segmentation algorithm selects a grayscale threshold based on the grayscale histogram of an image, divides the grayscale level of the image into several parts through one or more thresholds, and recognizes pixels belonging to the same part as the same object. However, this segmentation algorithm is sensitive to noise such as lighting. Based on the comparison of the advantages and disadvantages of the two methods, this paper combines them to segment vehicle targets. Firstly, using the visual saliency algorithm to detect the suspected target area as a whole, the maximum inter class variance method is used to obtain an appropriate threshold for binary segmentation. Secondly, a line-marking operation is performed under a given length in the same area of the binary image. Finally, the obtained line and its label (foreground or background) are input into the GrabCut algorithm to continue iteration until the real target is segmented [28].