Wildlife fence road ecological dataset in South Africa

This dataset is a comprehensive dataset of wildlife fence images, containing both electric and non-electric fences, to support computer vision, machine learning, and deep learning research in classification, object detection, semantic segmentation, and instance segmentation. Images were collected using a drone and a still camera in natural sunlight. Images were captured at varying viewpoints, distances, and angles to add diversity and practicality, resized and saved in PNG format. For classification purposes, the dataset is divided into folders for single-fence and double-fence configurations and the images are stored in their respective folders. For segmentation tasks, images were zoomed in and resized to focus on fence parts such as insulators, and polygon-based annotations were generated using the VGG Image Annotator (VIA). These annotations were converted into binary masks to enable pixel-level learning tasks. Thus, the dataset consists of the original images, VIA JSON annotation files, and instance-level and semantic segmentation masks obtained during preprocessing. The dataset is accompanied by Python scripts that automate the conversion of VIA polygon annotations into binary masks and the organisation of the dataset into a machine-learning-ready directory structure. This enables researchers to easily adapt the dataset for applications like semantic segmentation, instance segmentation, object detection, object counting, and classification. This dataset can serve as a valuable basis for future research in road ecology, wildlife conservation, and intelligent transportation systems, especially for developing automated fence recognition and monitoring systems to reduce wildlife-vehicle collision hotspots.本数据集包含211张图像，均通过独立式相机（standalone camera）与无人机（drone）采集，存储格式为JPEG（JPEG），分辨率均为512×512像素。该图像数据集包含105张单围栏与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；相较之下，独立式相机采集的159张图像中包含79张单围栏与80张双围栏图像。本数据集被划分为静态数据集（still dataset）与航拍数据集（aerial dataset）两类：静态数据集仅包含独立式相机采集的图像；无人机采集的航拍图像涵盖了保护区内的野生动物围栏与空中场景。本数据集可用于开发用于分类野生动物围栏为单围栏或双围栏类型的深度学习算法（deep learning algorithms）。本研究涉及的单、双围栏均为带电野生动物围栏。当机器学习算法（machine learning algorithms）检测到带电围栏时，表明保护区内存在狮子等危险动物，道路使用者途经该路段时需采取额外防范措施。此外，双围栏可在内层围栏失效时提供双重防护，有效防止野生动物逃逸。