Citation

Feng-Hui W, Ling-Yi L, Yong-Tao L, Shun T, Lang W. Int. J. Crashworthiness 2021; 26(5): 537-548.

Copyright

(Copyright © 2021, Informa - Taylor and Francis Group)

DOI

10.1080/13588265.2020.1764719

PMID

unavailable

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.

Language: en

Keywords

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