  As the population grows and more land is being used for urbanization,
ecosystems are disrupted by our roads and cars. This expansion of
infrastructure cuts through wildlife territories, leading to many instances of
Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue
that is having a global socio-economic impact, resulting in billions of dollars
in property damage and, at times, fatalities for vehicle occupants. In Saudi
Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC)
being particularly deadly due to the large size of camels, which results in a
25% fatality rate [4]. The focus of this work is to test different object
detection models on the task of detecting camels on the road. The Deep Learning
(DL) object detection models used in the experiments are: CenterNet,
EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that
CenterNet performed the best in terms of accuracy and was the most efficient in
training. In the future, the plan is to expand on this work by developing a
system to make countryside roads safer.
