A dissected dataset for single and double wildlife fences in South Africa

The dataset consists of 211 images collected using a standalone camera and a drone, stored in jpeg format with a height and width of 512 and 512 pixels, respectively. The image dataset contains 105 single fences and 106 double fences. The drone camera images included 26 single fences and 26 double fence images. In contrast, the standalone camera has 159 images with 79 single and 80 double fences. We labeled the datasets as either still or aerial datasets. The still dataset contains only images that used a standalone camera; the drone captured aerial images of wildlife fences and scenes from the sky. The images can be used to develop deep learning algorithms that classify whether a wildlife fence is single or double. This study uses wildlife electric fences for both single and double. When machine learning algorithms detect electric fences, dangerous animals, such as lions, are in the game reserve, and road users expect to take extra precautions while using the road. In addition, the double fence ensures high double protection in case of failure of the inner fence to protect wildlife from escaping.

本数据集包含211张图像，均通过单机相机与无人机采集，存储格式为JPEG，分辨率均为512×512像素。该图像数据集共包含105张单围栏图像与106张双围栏图像，其中无人机相机采集的图像包含26张单围栏与26张双围栏图像。相较而言，单机相机采集的图像共159张，含79张单围栏图像与80张双围栏图像。我们将该数据集划分为静态数据集与航拍数据集两类：静态数据集仅包含单机相机采集的图像；无人机则从空中采集野生生物围栏及相关场景的航拍图像。本图像集可用于开发用于判别野生生物围栏为单围栏还是双围栏的深度学习算法。本研究所涉及的单、双围栏均为野生生物电子围栏。当机器学习算法检测到电子围栏时，表明保护区内存在狮子等危险野生动物，此时道路使用者在通行时需采取额外防护措施。此外，双围栏可在内围栏失效时提供双重防护，避免野生生物逃逸。