UAV-Assisted Intelligent Traffic Diagnosis System Design

Abstract

Traffic flow is one of the most important information for traffic management. Traditionally, the data were obtained only from intersection monitors, which lack a macroscopic view of the entire roadway. Recently, the applications of unmanned aerial vehicles (UAV) have been widely applied to many fields and have become popular. Due to the programmable path planning and 3D movement characteristics of UAVs, we integrate edge computing for image recognition processing with UAVs to perform traffic flow analysis. This study successfully developed a prototype system to analyze the road segment video that is recorded from UAV-mounted cameras. A deep learning technique will be used to perform vehicle identification and tracking tasks. The average vehicle speed and vehicle flow can then be determined. In addition, violation event detection (including speeding, illegal parking, etc.) can also be reported. The system will automatically produce the diagnosis report. It can greatly reduce the burden of traditional manual image viewing, and the analyzed results can be used for traffic management units to improve traffic strategies.