# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 4 Proposed Study
### Proposed Feature Extraction Models
#### 4.2.3 Proposed Hybrid Fusion Model  
Algorithm 2 explains how the color value of a pixel is examined in image data. Figure 8 illustrates the functionality of the proposed hybrid image fusion model. In  
Figure 6: Flow of proposed edge detection model. the first stage, the sentinel 2D images are acquired from different sensors. Then the image is normalized using the weighted median filter method to convert 2D pixel intensity to 3D pixel intensity. After normalization, the mandatory features, namely color, edge, height, and width, are extracted from the images using the proposed edge and color detection models. In the third step, appropriate proposed projection and transformations are applied to convert 2D pixel and wavelength intensities to corresponding 3D information. The data obtained are successfully fused with a sample 3D image. This mechanism converts the acquired 2D image to a 3D image. The obtained multispectral 3D image lacks in its clarity related to the RGB colors. In order to improve the RGB color intensities, the obtained 3D image information from the suggested fusion model is integrated with the Brovey fusion method, which increases the RGB color intensity of the fused image to an appreciable extent [42,43]. As discussed in the above paragraph, the suggested hybrid fusion model involves key matrix and vector operations such as projection, transformation, and transposition. ```
1 Input
2D (or) 3D image
3 Assign Variables
4A: No of the wavelength of image
5G_Val: Array to store Gray values of the image
6 Chr_Val: Array representing genetic chromes
7 BFit: Best Fitness function
8 CF: Cross Over Function
9 Step1: If (input image is 2D use template to convert to 3D)
10 Else
11 For i = 1 to Sizeof(A)
12 Step 2: G_Val[i] = Convert pixel values to corresponding Gray values using proposed templates
13 End For
14 Step 3: Assign an 8-bit string to Chr_Val
15 Step 4: Randomly generate 30 sets of templates using values of \(G\_Val\)
16 Step 5: Substitute in the Equation (2)
17 Step 6: Estimate the best fitness function (BFit)
18 For i = 1 to Sizeof(A)
19 Step 7: If (G_Val[i] > BFit)
20 Edge Identified
21 End For
22 Else
23 Step 8: Compute (CF) for different templates using Equation (3)
24 Step 9: If (CF > 1)
25 Step 10: Switch to the template which has the highest CF value
26 Step 11: Repeat Step 2
27 Step 12: Else
28 Step 13: Switch to the next template
Table 2 describes the mandatory parameters used in the proposed image fusion model. Figure 9 illustrates the flow of the proposed fusion model. Initially, the 2D sentinel model acquired is normalized using the weighted mean filter method. This process converts the 2D pixel to 3D pixel information. Later, to convert the 2D image to a 3D image, the study uses advanced vector projections and matrix transformations. Finally, to acquire the original 3D image the research uses the QR(0) decomposition method to obtain the inverse matrix information of the fused 3D image. Figure 8: Proposed hybrid fusion framework. Figure 7: Proposed color estimation technique. Before processing the fusion process initially, the intensity of each wavelength is subtracted from the values of both the 2D sentinel and 3D images. The mean intensity wavelength for both the 2D and the 3D images is calculated using the weighted mean filter method. In terms of mathematics, the weighted mean filter (WMF) is equivalent to global optimization. It can successfully filter images without causing significant edge blur. Within a local window, it is an operator that replaces the current pixel with the weighted median of nearby pixels. In processing pixel (p) in the image (I), only pixels inside the local window, R(p), of radius (r) centered at (p) are considered. WMF connects each pixel, \(q\)\(\in\)\(R(p)\), with a weight, \(\alpha\)\(\in\)\(pq\), in the appropriate feature map (f), i.e.,

\[\alpha\)\(\in\)\(pq=g(f(p),f(q))\) (4)

where \(f(p)\) and \(f(q)\) are features of pixels \(p\) and \(q\), and \(g\) is a typical influence function between \(p\) and \(q\).