为夜间动物花纹识别构建了10类共5万张灰度图数据集。
SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms
- 构建10类动物花纹的32x32灰度图像数据集
- 每类5000张，总计5万张，含4万训练+1万测试图
- 专为夜间野生动物识别设计，适合模型评估
基于动物独特体表花纹（如条纹、斑点）在夜间图像中进行识别是计算机视觉中的复杂任务。现有方法多依赖颜色信息，但在夜间图像中该信息常缺失，导致花纹识别困难。然而，夜间识别对野生动物监测、生物多样性保护等应用至关重要。为此，本文提出SPOTS-10数据集，旨在解决这一挑战并提供机器学习算法的评估资源。该数据集包含10种动物的灰度图像，共计50,000张32×32像素的图像，每类5,000张，其中40,000张用于训练，10,000张用于测试。数据集已开源，可通过项目GitHub页面克隆获取：https://github.com/Amotica/SPOTS-10.git。
原文摘要 · Abstract (English)
Recognising animals based on distinctive body patterns, such as stripes, spots, or other markings, in night images is a complex task in computer vision. Existing methods for detecting animals in images often rely on colour information, which is not always available in night images, posing a challenge for pattern recognition in such conditions. Nevertheless, recognition at night-time is essential for most wildlife, biodiversity, and conservation applications. The SPOTS-10 dataset was created to address this challenge and to provide a resource for evaluating machine learning algorithms in situ. This dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species. Specifically, SPOTS-10 contains 50,000 32 x 32 grayscale images, divided into ten categories, with 5,000 images per category. The training set comprises 40,000 images, while the test set contains 10,000 images. The SPOTS-10 dataset is freely available on the project GitHub page: https://github.com/Amotica/SPOTS-10.git by cloning the repository.
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