## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
The visible light image recognition target box is defined as \(A\), the infrared image recognition target box as \(B\), the intersecting part of the two boxes as \(C\), and the merging part of the two boxes as \(D\), \(IOU=\frac{A\cap B}{\Lambda\cup B}=\frac{C}{D}\). The diagram of _IOU_ is shown in Figure 1.

The process of multi-source information fusion and _IOU_ judgment method is shown in Figure 2.

Figure 1: The diagram of _IOU_. When the visible light image detection box overlaps with the infrared image detection box, the _IOU_ value between two detection boxes is calculated. When the _IOU_ exceeds a certain threshold, it is determined that there is a recognition target in the area, and the visible light image detection box is used as an image block for target recognition. When the _IOU_ falls below this threshold, both detection boxes are discarded. When there is only an infrared image detection box and no visible light image detection box, it is determined as a ground infrared interference object and discarded. When there is only a visible light image detection box without an infrared image detection box, it is determined that the visible light image detection box has misidentified the target and is discarded. ## 4 Fusion Image Target Recognition Based on Bayesian Inference Method

Bayesian inference is an optimization method based on statistical probability and good robustness, and is less affected by noise and other factors, which can associate the prior probability of events with the posterior probability and represent the causal relationship between attributes through probability, which is a classic classification reasoning technique based on [PERSON] theorem. Bayesian classification algorithms are mainly divided into Bayesian Network (BN), Naive Bayes Classifier (NBC) and Dynamic Bayesian Network (DBN). There may be occlusion, jitter and other phenomena during the shooting of the imaging system, resulting in an incomplete display of target information in the image or the image being blurred and unclear. In order to simplify this problem, naive Bayesian inference is adopted. The naive Bayesian model is proposed by combining Bayesian theory and conditional independence assumptions to contain only one root node and multiple leaf nodes, and there is no association relationship between the leaf nodes, so according to the conditional independence between leaf nodes, the structure is the simplest Bayesian classification model; the naive Bayesian network structure is shown in Figure 3.

Figure 2: Multi source information fusion and _IOU_ judgment. If the target image taken by the test system is occluded, in order to further recognize the vehicle target, the Bayesian inference method is used to recognize the vehicle. Events \(f\) and \(s\) are defined; event \(f\) indicates whether the captured image contains vehicle target information, \(f\) takes a value of \(\{0,1\}\), \(f=1\), and event \(f\) is true, that is, the captured image contains vehicle target information. Otherwise, \(f=0\), event \(f\) is false. Event \(s\) indicates that the image to be recognized contains attributes of other targets, including vehicles, buildings, and grass, represented by \(s=\{car,building,grass\}\). The value of the vehicle attribute is \(\{0,1,2\}\), and when the value is \(0\), it means that the image to be recognized does not contain a vehicle target. When the value is \(1\), it means that the image to be recognized contains a vehicle target, but the vehicle target is occluded. When the value is \(2\), it means that the image to be recognized contains a vehicle target, and the vehicle target is not occluded. Similarly, the values of the buildings and grass attributes are \(\{0,1\}\), and when the value is \(0\), it means that buildings or grass are not detected in the captured image. Otherwise, buildings or grass are detected in the captured image.