Performance Evaluation of Edge Computing-Based Deep Learning Object Detection

Abstract

This article presents a method for implementing the deep learning object detection based on a low-cost edge computing IoT device. The limit of the hardware is a challenge for working the pre-trained neural network model on a low-cost IoT device. Hence, we utilize the Neural Compute Stick (NCS) to accelerate the neural network model on a low-cost IoT device by its high efficiency floating-point operation. With the NCS, the low-cost IoT device can successfully work the pre-trained neural network model and become an edge computing device. The experimental results show the proposed method can effectively detect the objects based on deep learning on an edge computing IoT device. Furthermore, the objective experiment demonstrates the proposed method can immediately infer the neural network model for images in average 1.7 seconds with only one of the NCS and the neural network model can reach average 9.2 fps for the video sequences with four NCSs acceleration. In addition, the discrepancy of the neural network model between the edge device and the edge server is less than 2% mean average precision (mAP).