Nighttime object detection system with lightweight deep network for internet of vehicles

Autonomous driving systems in internet of vehicles (IoV) applications usually adopt a cloud computing mode. In these systems, information got at the edge of the cloud computing center for data analysis and situation response. However, the conventional IoV face enormous challenges to meet the requirements in terms of storage, communication, and computing problems because of the considerable amount of information on the traffic environment. The environment perception during the nighttime is poorer than that during the daytime that this problem also requires addressing. To solve these problems, we propose a nighttime object detection scheme based on a lightweight deep learning model in the edge computing mode. First, the pedestrian detection and the vehicle detection algorithm that using the thermal images based on the YOLO architecture. We can implement the model on edge devices that can achieve real-time detection through the designed lightweight strategy. Next, a spatial prior information and temporal prior information into the detection algorithm and divide the frames into key and non-key frames to increase the performance and speed of the system simultaneously. Finally, we implemented the detection network for performance and feasibility verification on the Jetson TX2 edge device. The experimental results show that the proposed system can achieve real-time and high-accuracy object detection on edge devices.