Computer Vision for a Camel-Vehicle Collision Mitigation System

TL;DR

This paper evaluates various deep learning object detection models for identifying camels on roads to mitigate Camel-Vehicle Collisions, aiming to enhance road safety in regions with high wildlife-road interactions.

Contribution

It compares the performance of multiple deep learning models for camel detection, identifying CenterNet as the most accurate and efficient for this application.

Findings

CenterNet achieved the highest accuracy among tested models.

CenterNet was the most efficient in training.

The study provides a foundation for developing collision mitigation systems.

Abstract

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.