ドローンと深層学習による野生動物フェンス検出・認識で高速道路の安全意識を高める
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
ドローンとコンピュータビジョンを用いて野生動物用フェンスを検出・認識し、野生動物と車両の衝突を減らすための注意喚起を目指した研究。
著者: Irene Nandutu, Marcellin Atemkeng, Patrice Okouma, Nokubonga Mgqatsa, Jean Louis Ebongue Kedieng Fendji, Franklin Tchakounte
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.