# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 4 Case Study: Real-Time Power Line Inspection
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specifications**} & \multicolumn{3}{c}{**Evaluation Platforms**} \\ \cline{2-4}  & **Raspberry Pi 4B** & **Jetson Nano** & **Jetson Orin Nano** \\ \hline \multirow{3}{*}{CPU} & 1.5 GHz quad-core & Quad-core ARM A57 & Quad-core ARM A57 \\  & Cortex A-72 & @1.43 GHz & @1.43 GHz \\ \cline{1-1} \cline{2-4} Hardware Accelerator & - & 128-core Maxwell GPU & 1024-core Ampere GPU \\ \cline{1-1} \cline{2-4} RAM & 8 GB LPDDR4 & 4 GB 64-bit LPDDR4 & 8 GB 128-bit LPDDR5 \\ \cline{1-1} Storage & microSDHC card 64 GB & microSDHC 64 GB & microSDHC 64 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation platforms specifications. Figure 3: Deep learning framework for real-time power line inspection. The evaluation results are shown in Table 13. It shows that the detection processing time in Raspberry Pi 4B is significantly longer compared to other platforms, and is 16.4 seconds (s). From this result, we can understand that the YOLOv7 is not suitable for real-time detection as works on the Raspberry Pi 4B platform. However, some approaches can be taken to improve the performance, for example, by using the different edge devices such as Jetson Nano and Jetson Orin Nano. Our results show that YOLOv7 can achieve 0.33 s inference time using the FP16 quantized model on Jetson Nano with the TensorRT framework. Meanwhile, it can achieve 0.02 s inference time using the INT8 quantized model on Jetson Orin Nano with the TensorRT framework. On the other hand, using YOLOv7-tiny on Raspberry Pi4B can reduce the inference time to 2.74 s. YOLOv7-tiny can achieve 0.06 s inference time using the FP16 quantized model on Jetson Nano with the TensorRT framework, and it can achieve a 0.008 s inference time using the INT8 quantized model on Jetson Orin Nano with the TensorRT framework. In terms of accuracy, the usage of YOLOv7-tiny and model quantization technique does not reduce the mAP significantly. YOLOv7-Tiny INT8 model implementation on Jetson Orin Nano with TensorRT It can still achieve 0.936 mAP. Finally, in terms of RAM utilization and power consumption, the implementation with Raspberry Pi4B achieves the lowest RAM utilization and power consumption. From this result, we can also understand the impact of deep learning framework selection, for example, in Jetson Nano implementation. The implementation with TensorRT can reduce the inference time for every implementation case compared to PyTorch. For example, it can reduce the inference time for quantized YOLOv7 from 1.24 s to 0.33 s. For quantized YOLOv7-tiny, it can reduce the inference time from 0.23 s. to 0.06 s. This result can be obtained due to the hardware-specific optimization performed by TensorRT for NVIDIA GPUs, including those in Jetson devices [9]. In some previous work, researchers defined the metrics that define the balance between accuracy and complexity for practical usage. [PERSON] et al. defines an information density metric that evaluates the practical usage of an AI model by considering its accuracy and number of parameters [117]. On the other hand, [PERSON] proposed the NetScore metric, which asses the performance of a deep neural network in practical usage by considering the accuracy of the network, number of parameters in the network, and the number of