Edge-based street object detection

Abstract

Nowadays everything is becoming smart and intelligent with the help of Internet of Things (IoT) and artificial intelligence (AI). One of the promising applications of the integration of IoT and AI is smart city. The typical design pattern of smart cities is to install cameras and various sensors as many spots as possible and connect them to data center servers that can make smart decisions based on the inputs from the cameras and sensors. In such structure, network bandwidth may hinder the real-time processing because sensory data should be sent to the servers in the remote location before making any decision. To solve this problem, recent a few studies demonstrated edge computing. In edge computing, IoT devices can handle basic recognition. Thus, only sophisticated inputs that are not handled by IoT devices are sent to remote servers. In this paper, we demonstrate that the edge computing can provide stand-alone processing power for handling one of the fundamental applications of smart city, which is street object detection. Correct detection of various street objects is the core function of traffic systems and public safety applications of smart cities. A convolutional neural network model is developed by training with traffic data captured at California and Nebraska. Our model detects 14 objects with 25% average accuracy on NVIDIA Jetson TX2.