Citation

Alghamdi S, Algethami A, Tan T. Ain Shams Eng. J. 2024; 15(1): e102328.

Copyright

(Copyright © 2024, Ain Shams University, Publisher Elsevier Publishing)

DOI

10.1016/j.asej.2023.102328

PMID

unavailable

Abstract

Vehicle-camel collision is a persistent issue in countries where population of camels is high such as Saudi Arabia. The purpose of the research is to introduce a new solution to eliminate this issue. Previous solutions, such as fencing the sides of the roads, designing better camel warning signs and fining camel owners when camels cross high traffic roads, are either expensive, ineffective, or hard to implement. Therefore, in this work, we harness the power of deep learning to tackle this problem. In particular, we use state-of-the-art deep learning object detectors to detect camels on roads with high accuracy.

RESULTS show that all implemented models were capable of detecting camels on or near roads. Moreover, the single-stage detector Yolo v3 was found to be the most accurate and is as fast as its successor Yolo v4.

FINDINGS of this work helped select the deep learning model needed for a reliable and automatic vehicle-camel collision avoidance system.

Language: en

Keywords

Object detection; Vehicle-camel collision; Yolo v3; Yolo v4