###### Abstract
Additional evaluation parameters will be investigated to enhance the studies, including resource consumption such as energy consumption, inferencing overhead, memory utilization, and processing complexity. Resource-aware IoT nodes with tiny system elements will be used for analysis. Due to limited processing functionality, the paper \"A Deep Learning Framework Performance Evaluation to Use YOLO in Nvidia Jetson Platform\" highlights the challengesof implementing deep learning-based object detection technology on embedded systems and mobile devices [8]. The paper proposes a performance inference method that fuses the Jetson monitoring tool with TensorFlow and TensorRT source code on the Nvidia Jetson AGX Xavier platform to address this. The study compares the performance of different deep learning frameworks, including TensorFlow, TensorFlow-TensorRT, TensorRT, and TensorFlow-Lite, in terms of CPU utilization, GPU utilization, object accuracy, latency, and power consumption, using the You Look Only Once Version4 (YOLOv4) model and Common Objects in Context (COCO) and PASCAL Visual Object Classes (VOC) datasets. The study shows that TensorFlow results in high latency, while TensorFlow-TensorRT and TRT using Tensor Cores provide the most efficiency. However, TF-Lite showed the lowest performance due to its limited GPU capability. The study concludes that the proposed performance inference method can efficiently develop deep learning-related object detection technology on the Nvidia Jetson platform or desktop environment. In conclusion, this paper aimed to optimize deep learning-based object detection technology for low latency, high accuracy detection rates, and low power consumption in embedded systems. The study applied TensorFlow and TensorRT, commonly used deep learning frameworks in Nvidia Jetson AGX Xavier embedded systems. It compared them using a fusion monitoring tool that measured CPU utilization, GPU utilization, object detection accuracy, latency, and power consumption. The study found that TensorFlow used on desktops can be applied to Nvidia Jetson AGX Xavier, but it has the highest power consumption and high latency. On the other hand, TF-Lite operates efficiently only on mobile devices, while TF-TRT and pure TRT are the most efficient for embedded systems with built-in Tensor Cores. The study recommends using pure TRT if there is insufficient implementation knowledge of the TensorFlow deep learning framework. Future work will involve studying the efficient use of deep learning frameworks with large datasets such as Open Images and Image Net by applying PyTorch libraries to Nvidia Jetson AGX Xavier. The courier and delivery service market continues to thrive despite economic crises worldwide. With 70% of parcels being delivered in metropolitan areas, this paper [9] proposes developing an eco-friendly underground logistics system (ULS) to handle freight volume in the centers of cities. The study focused on analyzing damaged parcel boxes within the ULS and utilized a convolutional neural network (CNN), MobileNet, to detect and classify them. The Google Colaboratory notebook was used for image classification, and 4882 images were collected for the experiment. Based on test time, the MobileNet algorithm was superior to other image classification models, VGG16 and ResNet50. The results show that MobileNet can be used to ensure the reliability and safety of parcel boxes in a ULS. Thus, MobileNet can potentially be used to identify damaged boxes. In conclusion, this paper proposes using an underground logistics system (ULS) in metropolitan areas to reduce the number of freight cars and handle freight volume. An experiment was conducted to detect whether the parcel boxes were damaged or not using deep learning methods to improve the reliability and safety of parcel boxes. The MobileNet algorithm was used for image classification, and the results show that it performs relatively better than other image classification models concerning test time.