# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Configuration of Training Hyperparameters through Pre-Training  
In this paper, Faster R-CNN [43] is adopted as the deep learning mode for utilization. Faster R-CNN, a product of evolutionary progression within the R-CNN family, gained  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Type** & **Pre-Training Dataset Type** & **Number of Images** & **Number of Car Objects** \\ \hline \multirow{3}{*}{Train} & Pre-training & 336 & 1141 \\  & Validation & 85 & 246 \\ \cline{1-1}  & Total & 421 & 1387 \\ \cline{1-1}  & - & 106 & 373 \\ \cline{1-1}  & Total & 527 & 1760 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of the labeled dataset and its subdivision for training and validation.  
substantial prominence following its release in 2015, which was attributed to its heightened ability to swiftly and accurately propose potential objects in comparison to the pre-existing Fast R-CNN [44]. This enhanced capability is attributed to the integration of a region proposal network (RPN) into the architecture. the intricate structure of Faster R-CNN reveals its underlying operational efficacy. It is noted that the adopted deep learning algorithm is not an important factor in this paper, because the algorithm is used for comparative purposes in the same training environment.  
The primary input to this model comprises an image, subsequently subjected to the process of feature map extraction via CNN procedures. Within the intermediate phase of processing, the RPN takes center stage, effectively generating potential bounding boxes through a combination of objectness and regression analyses. These initially proposed bounding boxes then undergo a post-processing phase, facilitated by non-maximum suppression (NMS) [45]. Furthermore, the terminal component of the architecture, the fully connected layer (FC layer), plays a pivotal role in ascertaining the object's categorical classification and precisely determining the coordinates of its bounding box.  
Since this study considers the impact of perspective at low CCTV height, the overlapping phenomenon of many objects is highly visible. Therefore, the hyperparameters, epoch and NMS intersection-over-union (IOU) threshold, which are sensitive to it, were selected for sensitivity analysis and calibration through preliminary training. An epoch is defined as the training unit of a deep learning model, which is one epoch when the entire train dataset is trained once [46]. In general, the number of training epochs until the loss function of a deep learning model is converged should be determined in advance. The NMS IOU threshold is a hyperparameter used by the NMS module, which checks the overlap ratio between the bounding boxes to be post-processed, and removes them as duplicates when the threshold is exceeded. In this paper, NMS removes the overlapping bounding boxes for the proposals in the RPN stage. In this case, depending on the NMS IOU threshold, bounding boxes with less than 10 to 20% overlap are passed to the FC layer, or bounding boxes with more than 80 to 90% overlap are passed to the FC layer, affecting the weight update of Faster R-CNN. Other layouts of training configurations adopted in this study are summarized in Table 4.