Real-Time Road Accident Scene Recognition Using Computer Vision Applied to Drone Imagery

Publication Type

Journal Article

Publication Date (Issue Year)

2024

Journal Name

2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES

Abstract

Rapid urbanization has led to a variety of problems in road traffic, such as recurrent congestion and traffic accidents. To mitigate these problems, it is necessary to monitor and analyze traffic volumes in order to anticipate or detect anomalies in road traffic. This work proposes a solution for traffic accident scene recognition using computer vision applied to drone imagery. A Mavic Air 2S drone collects geo-referenced aerial images of the portion of road to be monitored, and the data is annotated, trained and tested on Roboflow. The results of the experiment give an overall object detection rate of 95.3%, accuracy of 94.4% and recall of 91.3%. Geographical coordinates of images containing accident scenes are extracted and stored in a database for statistical and rescue purposes.

Keywords

Accident scene, Computer vision, Drone imagery, Real-time Recognition

Rsif Scholar Name

Adama Coulibaly

Rsif Scholar Nationality

Burkina Faso

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Gaston Berger (UGB), Senegal

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