## 4 Proposed Methodology
### Ensemble Learning
for\(D\) to all \(B\) listdo  Calculate the IOU (Intersection over Union) of this proposal \(D\) with every other proposal \(B\). ifthe IOU is greater than the threshold \(N\)then  1. Remove that proposal from \(B\). 2. Repeat: take the proposal with the highest confidence from the remaining proposals in \(B\) and remove it from \(B\) and add it to \(D\). else  Once again calculate the IOU of this proposal with all the proposals in \(B\) and eliminate the boxes which have higher IOU than the threshold. end if  This process is repeated until there are no more proposals left in \(B\). end for. ```  
**Algorithm 4**Proposed Ensemble NMS (Non-Maximum Suppression)  
```
Input :A list of filtered proposals \(D\). The proposed Algorithm: Select the proposal with the highest confidence score. Remove it from \(B\) and add it to the final proposal list \(D\) (Initially \(D\) is empty). Now compare this proposal with all the proposals. for\(D\) to all \(B\) listdo  Calculate the IOU (Intersection over Union) of this proposal \(D\) with every other proposal \(B\). ifthe IOU is greater than the threshold \(N\)then  1. Remove that proposal from \(B\). 2. Repeat: take the proposal with the highest confidence from the remaining proposals in \(B\) and remove it from \(B\) and add it to \(D\). else  Once again calculate the IOU of this proposal with all the proposals in \(B\) and eliminate the boxes which have higher IOU than the threshold. end if  This process is repeated until there are no more proposals left in \(B\). end for. ```  
**Algorithm 5**Proposed Ensemble NMS (Non-Maximum Suppression)  
Once again calculate the IOU of this proposal with all the proposals in \(B\) and eliminate the boxes which have higher IOU than the threshold. end if  This process is repeated until there are no more proposals left in \(B\). end for. ```
**Algorithm 6**Proposed Ensemble NMS (Non-Maximum Suppression)
``` Input :A list of filtered proposals \(D\). The proposed Algorithm: Select the proposal with the highest confidence score. Remove it from \(B\) and add it to the final proposal list \(D\) (Initially \(D\) is empty). Now compare this proposal with all the proposals. for\(D\) to all \(B\) listdo  Calculate the IOU (Intersection over Union) of this proposal \(D\) with every other proposal \(B\). ifthe IOU is greater than the threshold \(N\)then  1. Remove that proposal from \(B\). 2. Repeat: take the proposal with the highest confidence from the remaining proposals in \(B\) and remove it from \(B\) and add it to \(D\). else  Once again calculate the IOU of this proposal with all the proposals in \(B\) and eliminate the boxes which have higher IOU than the threshold. end if  This process is repeated until there are no more proposals left in \(B\). end for. ```
**Algorithm 7**Proposed Ensemble NMS (Non-Maximum Suppression)  
Once again calculate the IOU of this proposal with all the proposals in \(B\) and eliminate the boxes which have higher IOU than the threshold. end if  This process is repeated until there are no more proposals left in \(B\). end for. ```

**Algorithm 8**Proposed Ensemble NMS (Non-Maximum Suppression)

After generating multiple bounding box predictions, YOLO applies Non-Maximum Suppression (NMS) to eliminate redundant boxes for the same object as shown in Algorithm 2. NMS selects the bounding box with the highest confidence score and removes other boxes that have a high IoU with the selected box as follows in (12). \[\text{IoU}=\frac{\text{Area of Overlap}}{\text{Area of Union}} \tag{12}\]

where the input is considered the image is divided into grid cells. For each grid cell, YOLO predicts bounding boxes, confidence scores, and class probabilities that consider the output. The loss function consists of bounding box regression, object confidence, and classification losses. NMS suppresses boxes with an IoU above a certain threshold as in subsequent steps. 1. Sort bounding boxes by a confidence score. 2. For each box, calculate IoU with boxes ranked below it. 3.