## 3 Visible Light and Infrared Image Feature Fusion Algorithm
### Visible Light Image Recognition Algorithm
The steps for obtaining the segmentation threshold algorithm (Algorithm 3) using the maximum inter class variance method are as follows:  
``` Step 1:

**Algorithm 3** Segmentation Threshold Algorithm

### Infrared Image Feature Extraction

Any object with a temperature above absolute zero can produce thermal radiation, while the spectrum of thermal radiation produced by an object with a temperature below 1725 degrees Celsius is concentrated in the infrared light region, so all objects in nature can radiate infrared energy outward, and the wavelength of infrared energy released by objects at different temperatures is not the same. Therefore, the infrared wavelength is related to the temperature. The wavelength and distance of infrared radiation produced by any object due to its own physical and chemical properties and different temperatures are also different. Compared with visible light images, infrared images and environments are clearly different, but they also have the characteristics of poor resolution, low signal-to-noise ratio, blurred visual effects, and a nonlinear relationship between grayscale distribution and target reflection features. Therefore, using the local invariant features of infrared images, connected area feature extraction is adopted in our feature extraction method, the image is divided into several components, each part's features are extracted, and finally the same feature area is integrated. These obtained infrared images are converted into a grayscale image, and a grayscale level filter is added. The grayscale is divided into 17 levels, corresponding to multiples of 15 from 0 to 255. The grayscale value in each grayscale interval is set to be equal to the highest value of that level, highlighting the sense of hierarchy in the grayscale image. The detection intensity of infrared images is divided into three levels: grayscale values between 210 and 240 represent strong radiation, grayscale values between 120 and 180 represent moderate radiation, and grayscale values below 90 represent ordinary radiation. Strong radiation mainly comes from vehicle targets, moderate radiation mainly comes from buildings, and ordinary radiation comes from the normal infrared intensity of the surrounding environment. To corrode and expand without damaging the original target information of the infrared image, a 3 \(\times\) 3 operational filter is used to partially eliminate the interference of ground infrared noise. The infrared feature extraction algorithm is

\[ \begin{array}{c}\gamma\left(R_{r},\theta_{gray},\theta_{open}\right)=\left\{ R_{r1},R_{r2},\cdots,R_{rj}\right\}\\ R_{ri}=\left\{G^{{}^{\prime}}_{i},S_{i}|i=1,2,3,\cdots,j\right\}\end{array}  \tag{3}\]

In Formula (3), \(R_{r}\) represents the infrared image, \(\theta_{gray}\) represents the grayscale filter, \(\theta_{open}\) represents the open operation filter, \(R_{rj}\) is the recognized infrared target image block, \(G^{{}^{\prime}}_{i}\) is the position information of the image block, and \(S_{i}\) is the radiation level of the image block [29]. ### Information Fusion Based on IOU Judgment

While visible light images have high resolution and good recognition contrast, infrared images are less affected by the environment, have high accuracy, and can recognize camouflage. The effectiveness of target recognition is improved by fusing infrared images and visible light images. Due to the difference in resolution between visible and infrared images, the mask boxes extracted from infrared images are scaled and mapped into the RGB image recognition results, resulting in box information that includes two methods of recognition. _IOU_ is used to evaluate the similarity between two rectangular boxes. The larger the value of _IOU_, the closer the predicted result is to the actual result, while the smaller the value, the worse the predicted result.