A UAV Based Multi-object Detection Scheme to Enhance Road Condition Monitoring and Control for Future Smart Transportation

Abstract

Road condition monitoring and control is essential for smart transportation in the era of autonomous driving. In this paper, we propose to apply unmanned aerial vehicle (UAV), wireless communications and artificial intelligence (AI) to achieve multi-object detection for smart road monitoring and control. In particular, the application of UAV enables real-time image view to monitor road condition, such as traffic flow and on-road objects, in an efficient way without disturbing normal traffic. Those raw image data are first offloaded to a road side unit through wireless communications. A computing platform connected to the road side unit can execute the AI based scheme for road condition monitoring and control. The AI based scheme is developed around convolutional neural network (CNN). For demonstration, the objects of interest considered in this work include advertisement billboards, junctions, traffic signs and unsafe objects. Other objects can be extended to the developed system with more collected data. To evaluate the proposed scheme, we launched a UAV to collect real-life road images from multiple road sections of a highway. The AI based scheme is then developed using portion of the raw data. Test of the AI scheme is conducted using the rest of the dataset. The evaluation results have demonstrated that the proposed UAV based multi-object detection scheme can provide accurate results to support efficient road condition monitoring and control in future smart transportation.