## 5 Result and Discussion
### Environment  
For the deep learning framework comparative analysis, AGX Xavier 16 GB, sold by Nvidia, was used. The OS is installed through SDK Manager provided by Ubuntu-based Nvidia, and version 32.6.1 is applied. To install CUDA, cuDnn, and TRT, install the Jetpack package version 4.6 to complete the deep learning-related library environment setup. [35]. The library version installed through the Jetpack package is shown in Figure 7.  
For high-performance inference speed and accuracy, MAXN mode is applied, which lifts the limit on CPU and GPU utilization [36]. TensorFlow was installed through the installation method provided by Nvidia, and version 2.6.2 applies [37]. Python version uses built-in 3.6.9 by default. The COCO dataset has 80 classes, and the number of images for training is 118,287 and the number of images for validation was 5000. Download the YOLOv4-Native model and the YOLOv4-Tiny model that have been trained through the COCO dataset [38; 39]. The PASCAL VOC dataset has 20 classes, and the total number of images for training and validation was 17,125. Since the PASCAL VOC dataset does not provide a trained YOLOv4 model, the data were downloaded directly and trained through the darknet deep learning framework [25; 26]. The environment used for training (Nvidia Jetson AGX Xavier for detection and performance inference measurements) is a desktop environment with Ubuntu 20.04 OS, DDR4 32 GB memory, and NvidiaSuper GPU. The learning process first converts the data annotated in the PASCAL VOC format into the YOLO format using the [40] source code. Second, the converted YOLO format file and PASCAL VOC image data are copied into one folder. Third, after copying, the data for training and verification are divided in an 8:2 ratio, and the value when divided into nine grid cells in the YOLO model is calculated through the calc_anchors command provided by darknet. Fourth, input the output anchors value in the configuration file yolov4-custom.cfg, input the classes factor in the configuration file as 20, and input the filters factor as 75, because it is calculated as (number of classes + 5) \(\times\) 3. Fifth, the width and height of the neural network were input as 416 \(\times\) 416, the same as COCO. Finally, we trained through darknet's train command, and default values were applied to other input parameters, such as learning rate and max batches [26].  
The source code uses [32], which can evaluate the YOLOv4 model using the TensorFlow deep learning framework. In [32], the YOLOv4-Native.weights and YOLOv4-Tiny.weights files were converted into models required for comparative analysis because the code that can be converted to TF-Lite and TF-TRT was built-in. In the case of pure TRT, the source code provided in [32] is not available because it is a different structure from TensorFlow, so another source code [33] was used. In order to check the metrics generated during inference performance measurement, the monitoring source code [34] provided by Nvidia was applied and modified to [32; 33], and the values generated during inference were automatically saved in the form of a CSV file. When converting YOLOv4 to match the deep learning framework, the network size was configured as 416 \(\times\) 416 all the same. Details of the differences and source codes of the deep learning framework can be found in Section 4.  
The video used to measure inference performance has a resolution of 1280 \(\times\) 720 at 30 FPS and is 30 s long. The video is a right image of a low-light environment, two to three people are detected, and three cars are detected fixedly. For readers who are curious about the image of the sample video used, the screen captured during inference performance measurement is shown in Figure 8.  
Figure 7: Check various library versions that make up the experimental environment.