# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 4 Case Study: Real-Time Power Line Inspection  
In Section 3, the key considerations in object recognition development on edge computing devices have been discussed. In order to understand the real-implementation of the concepts and issues presented in previous sections, an example of an application in power transmission line inspection is presented in this section. This case study was selected by considering the need for real-time object recognition to be conducted on the edge devices that are installed on the drone [112; 113; 114]. The power transmission line should be regularly inspected to promptly identify and fix any damage to guarantee the effective and dependable transmission of high-voltage electricity. The traditional method, i.e., line crawling, is not safe because the possibility of the engineers falling from the wire or getting electrocuted is very high. Some other methods have been developed, such as using telescopes to observe the lines from the ground, power transmission line inspection robots (PTLIRs) [115], helicopter-assisted inspection, and automated helicopter-assisted inspection [116]. How \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Title** & **Used Metrics** \\ \hline
[107] & Energy-Efficient artificial intelligence of Things with Intelligent Edge objects or detections & Power consumption (watt), model loading time (ms), average processing time (ms), frame rate (FPS), energy consumption (Wh) \\ \hline
[23] & Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application & mAP, F1-score, FPS, FLOPS \\ \hline
[108] & Masked Face Recognition by Zeroing the Masked Region without Model Retraining & Accuracy, F1-score, feature extraction time, inference time \\ \hline
[109] & Low-Resolution Face Recognition for CCTV and Edge-Powered Smart Attendance Systems & Accuracy, precision, recall, F1-score, inference time (ms), RAM/CPU/GPU utilization \\ \hline
[110] & Edge artificial intelligence and super-resolution for enhanced weapon detection in video surveillance & Average precision 50 (AP50), Giga FLOPS (GFLOPS), latency (ms/img), F1-score \\ \hline
[111] & Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network & mAP, inference time \\ \hline
[44] & Low Latency YOLOv3-Tiny Accelerator for Low-Cost FPGA Using General Matrix Multiplication Principle & Hardware Utilization (BRAM, DSP, LUT, FF), gate counts conversion, latency, mAP, throughput (GOPS), power consumption \\ \hline
[45] & Fast and Scalable Multicore YOLOv3-Tiny Accelerator using Input Stationary Systolic Architecture & Hardware Utilization (BRAM, DSP, LUT, FF), gate counts conversion, latency, mAP, throughput (GOPS), power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Key performance metrics used in previous research. ever, these methods mainly depend on human observation, are less effective, and present high risk [55]. In our study, we develop a power transmission line inspection system using edge computing platforms [22]. The developed deep learning framework for this research is shown in Figure 3. It primarily consists of two stages, which are the training phase and the real-time detection phase. In the training phase, the data generation, data annotation, and training are conducted. In this study, YOLOv7 and Tiny YOLOv7 are trained. Both are state-of-the-art methods for the object detection task. In the real-time detection phase, the trained model is evaluated on edge computing devices, which are Raspberry Pi 4B, Jetson Nano, and Jetson Orin Nano. The specification of each device is defined in Table 12. Some deep learning frameworks are also evaluated in this research, i.e., OpenVINO 2023.1.0, PyTorch 2.1.2, and TensorRT 8.2.1. The model quantization to FP16 and INT8 are also evaluated in this research. Finally, the final evaluation results are the mAP, inference time, RAM utilization, and power consumption.