Road traffic accident scene detection and mapping system based on aerial photography

Abstract

Road traffic accident scenes provide useful information for understanding how accidents happen and calculating the speeds of the vehicles involved. Unmanned aerial vehicles can obtain photographs of accident scenes, but utilizing these photographs has problems such as low target resolution and scale changes. An improved Resnet–Single-Shot Multibox Detector (R-SSD) algorithm based on a deep residual network (Resnet) is presented to address these problems. A residual network with better characterisation ability is proposed to replace the basic network, and residual learning is employed to reduce difficulty in network training and improve target detection accuracy. The proposed aerial target detection algorithm, based on feature information fusion (I-SSD), addresses the problems of repeated detection and small-sample missed detection in the original SSD target detection algorithm. Based on the detection results, a road traffic accident scene mapping system using either AutoCAD or hand-drawing can be designed.

Keywords:

• Traffic accident scene
• aerial photography
• object detection
• scene mapping

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The study was supported by grants from the National Nature Science Foundation of China [Grant No. 2160701], the National Nature Science Foundation of Shannxi Province [Grant Nos. 2018JQ5142 and 2017JQ5121], and the Project of Basic Scientific Research Business Cost in Central Colleges and Universities [Grant No. 300102228106].