Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology

TL;DR

This study employs drone technology and deep learning to detect wildlife fences in South Africa, aiming to raise awareness and reduce wildlife-vehicle collisions by informing travelers about fence presence.

Contribution

It introduces a drone-based computer vision approach for wildlife fence detection, outperforming still camera methods and facilitating real-time awareness to mitigate collisions.

Findings

Drone camera models outperform still camera models in fence detection.

Poor performance linked to image over-decompression and camera detail limitations.

Model deployment on edge devices can effectively inform travelers about wildlife fences.

Abstract

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.