为澳洲自动驾驶研发提供756张高精度标注图像数据集
SydneyScapes: Image Segmentation for Australian Environments
- 采集悉尼及周边城市756张图像，完成像素级语义、实例与全景分割标注
- 涵盖澳洲典型环境特征，支持本地化算法训练与测试
- 公开可用，适配自动驾驶研究者与行业开发者使用
自动驾驶车辆已在中、美、德、法、日、韩、英等多国部分部署与测试，但在澳大利亚仍缺乏充分示范。机器学习在自动驾驶感知系统中的应用，亟需针对特定环境的本地标注数据集。为此，我们推出悉尼场景数据集（SydneyScapes），专为图像语义分割、实例分割和全景分割任务设计。该数据集采集自澳大利亚新南威尔士州（NSW）的悉尼及周边城市，包含756张具有高质量像素级标注的图像，旨在支持自动驾驶产业与研究者在澳洲环境下进行算法开发、测试与部署。此外，我们提供了基于前沿算法的基准测试结果，为后续研究提供参考。数据集已公开发布于 https://hdl.handle.net/2123/33051。
原文摘要 · Abstract (English)
Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with limited demonstrations in Australia. The integration of machine learning (ML) into AV perception systems highlights the need for locally labelled datasets to develop and test algorithms in specific environments. To address this, we introduce SydneyScapes - a dataset tailored for computer vision tasks of image semantic, instance, and panoptic segmentation. This dataset, collected from Sydney and surrounding cities in New South Wales (NSW), Australia, consists of 756 images with high-quality pixel-level annotations. It is designed to assist AV industry and researchers by providing annotated data and tools for algorithm development, testing, and deployment in the Australian context. Additionally, we offer benchmarking results using state-of-the-art algorithms to establish reference points for future research and development. The dataset is publicly available at https://hdl.handle.net/2123/33051.
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