An Approach to the Automatic Construction of a Road Accident Scheme Using UAV and Deep Learning Methods

Abstract

Recreating a road traffic accident scheme is a task of current importance. There are several main problems when drawing up a plan of accident: a long-term collection of all information about an accident, inaccuracies, and errors during manual data fixation. All these disadvantages affect further decision-making during a detailed analysis of an accident. The purpose of this work is to automate the entire process of operational reconstruction of an accident site to ensure high accuracy of measuring the distances of the relative location of objects on the sites. First the operator marks the area of a road accident and the UAV scans and collects data on this area. We constructed a three-dimensional scene of an accident. Then, on the three-dimensional scene, objects of interest are segmented using a deep learning model SWideRNet with Axial Attention. Based on the marked-up data and image Transformation method, a two-dimensional road accident scheme is constructed. The scheme contains the relative location of segmented objects between which the distance is calculated. We used the Intersection over Union (IoU) metric to assess the accuracy of the segmentation of the reconstructed objects. We used the Mean Absolute Error to evaluate the accuracy of automatic distance measurement. The obtained distance error values are small (0.142 ± 0.023 m), with relatively high results for the reconstructed objects’ segmentation (IoU = 0.771 in average). Therefore, it makes it possible to judge the effectiveness of the proposed approach.

Drones for Road Accident Management: A Systematic Review

“…Koshe et al [36] presented integration of machine learning and videogrammetry techniques to analyse road accidents using videos and images from UAVs, where the machine learning model classifies accident site images based on accident causes and videogrammetry employs SFM techniques to generate a point cloud mesh from the captured video. Saveliev et al [25] created a system that leverages UAV technology and deep learning methods to achieve precise measurements and automate the reconstruction process of road accidents with high accuracy.…”

Modeling the influence of external influences on the process of automated landing of a UAV-quadcopter on a moving platform using technical vision

“…В современном мире наблюдается растущий спрос на автономные технологии и беспилотные системы, которые находят широкое применение в различных областях, таких как сельское хозяйство [1], [2], транспортная промышленность [3], [4], картографирование местности [5], [6], обследование линий электропередач [7], сейсмология [8]. Особый интерес представляют мультироторные БпЛА с четырьмя винтами, известные как квадрокоптеры.…”

Analysis of UAV Flight Patterns for Road Accident Site Investigation

“…Through the application of an unmanned aerial vehicle (UAV), i.e., a drone [19][20][21][22][23], accident data recording time can be decreased. This is due to the fact that certain phases of the traditional data recording process can be completely replaced by a UAV flight.…”