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

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