## 4 Proposed Methodology
### Ensemble Learning
This provides a more comprehensive evaluation of the model's performance across different IoU thresholds. mAP50 is computed in the proposed model [33; 34]. It refers to the mAP calculated at an IoU threshold of 0.5. IoU is a measure of the overlap between the predicted bounding box and the ground truth bounding box. An IoU of 0.5 means the predicted bounding box and the ground truth bounding box overlap by 50%. So, mAP50 gives the average precision of the model at this IoU threshold. ### Result

Loss functions are employed in the proposed model in the training and validation phases. It is used to predict object positions, classifications, and other parameters with accuracy. Usually, the loss function is composed of several parts that assess various facets of prediction accuracy. In the proposed model are measured three losses, which are bounding regression loss, class prediction loss, and distribution focal loss [35]. Figure 2 shows train/valid loss for YOLO8. The train/box_loss is considered the loss incurred in predicting the bounding boxes of the objects during the training phase. A lower value indicates a better fit of the model to the training data. The proposed model, given the minimum train/box_loss is 1.2476 through 40 epochs. At the same time, the train/cls_loss is the classification loss during training. It measures how well the model is performing at classifying the objects. So, the proposed model given the minimum train/cls_loss is 0.69987 through 40 epochs. Finally, in the training phase, we measure the train/dfl_loss, where the loss is related to the distribution-focused learning during training. It is a measure of how well the model is learning the data distribution and is given 0.9827 [36]. In addition, we measure the loss in the validation phase (val/box_loss is 1.4334, val/cls_loss is 0.90363, and val/dfl_loss is 1.0912). The lower value indicates that the model generalizes well to unseen data, as shown in Figure 2.

Figure 3 shows train/valid loss for RT-DETR. The train/box_loss is 0.4146 through 15 epochs; train/cls_loss is 0.39282 and train/dfl_loss is 0.05091. The loss in the validation phase is where val/box_loss is 0.56202, val/cls_loss is 0.52314, and val/dfl_loss is 0.09694. Comparing Figures 1 and 2 explains the loss in RT-DETR is less than YOLO 8, despite the number of epochs in RT-DETR being the least. This is due to RT-DETR model complexity, while YOLO8 is simplicity and speed. Figure 3: Training and validation loss for RT-DETR. Figure 2: Train and valid loss for Yolo8. Optimizing the model's loss function and architecture resulted in an 18% reduction in training and inference time. Using a more efficient loss function allows the model to converge faster, lowering the number of epochs required for training. This not only speeds up training but also improves inference times, resulting in better overall performance. Figure 3(a) shows the Precision Metric and Recall Metric for YOLO8. The proposed model has a precision of 0.74206, which means it is quite good at not labeling a negative sample as positive. Also, Recall Metric has a recall of 0.54332, indicating its ability to find all the positive samples. Figure 3(b) shows precision metrics and recall metrics for RT-DETR. The proposed model has a precision of 0.8051, which means it is quite good at not labeling a negative sample as positive. Also, Recall Metric has a recall of 0.74484, indicating its ability to find all the positive samples. Figure 4(a) shows mAP50 and mAP50-95 metrics for YOLO8. The proposed model has an mAP50 of 0.57447, indicating a moderate ability to detect objects with a large overlap with ground truth and an mAP50-95 of 0.32494, indicating its performance across different levels of IoU thresholds is fair. Figure 4(b) shows mAP50 and mAP50-95 metrics for RT-DETR. The proposed model has an mAP50 of 0.73544, indicating a moderate ability to detect objects with a large overlap with ground truth. While the mAP50-95 of 0.4595 indicates its performance across different levels of IoU thresholds is fair.