Zebra Crossing Image Dataset

### **This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: [[e-mail]](mailto:[e-mail])**

This dataset is an extremely challenging set of over 3,000+ images of excavator vehicles from multiple construction site. These images captured and crowdsourced from over 200+ different locations, where each image is **manually reviewed and verified** by computer vision professionals at Datacluster Labs. It contains a wide variety of Zebra Crossings in the county.

### **Dataset Features**

- Dataset size : 3000+ images
- Captured by : Over 1000+ crowdsource contributors
- Resolution : HD and above (1920x1080 and above)
- Location : Captured with 200+ locations
- Diversity : Various lighting conditions like day, night, varied distances, view points etc.
- Device used : Captured using mobile phones in 2020-2022
- Usage : Objects on the road, self-driving vehicles etc.

### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [[e-mail]]([e-mail]) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**

**Note**:
All the images are manually captured and verified by a large contributor base on DataCluster platform.

此数据集由 DataCluster Labs 收集而成。若欲下载完整数据集或提交关于您新数据收集需求之申请，敬请发送邮件至：[[e-mail]](mailto:[e-mail])。该数据集是一组极具挑战性的挖掘机械车辆图像集合，包含超过 3,000 张图像，源自多个建筑工地。这些图像通过超过 200 个不同地点的采集和众包所得，每张图像均经 Datacluster Labs 的计算机视觉专业人士手动审查与验证。数据集中包含了该县多种多样的斑马线。

### **数据集特性**

- 数据集规模：3000+ 张图像
- 采集者：超过 1000+ 众包贡献者
- 分辨率：高清及以上（1920x1080 及以上分辨率）
- 采集地点：在 200+ 个地点采集
- 多样性：各种光照条件，如白天、夜晚，不同距离，不同视角等
- 设备：2020-2022 年间使用手机采集
- 用途：道路上的物体，自动驾驶车辆等

### **可用标注格式**

COCO，YOLO，PASCAL-VOC，Tf-Record

**为下载完整数据集或提交关于数据集需求之申请，敬请联系：[[e-mail]]([e-mail])。访问 [www.datacluster.ai](www.datacluster.ai) 了解更多信息。**

**注意**：所有图像均由 DataCluster 平台上的广大贡献者手动采集并验证。

搜集汇总

背景与挑战

背景概述

Zebra Crossing Image Dataset是一个专为自动驾驶研发设计的高质量斑马线图像集，包含3000+张高清图像，覆盖多样化的实际道路场景。该数据集由众包采集并经过专业验证，支持多种标注格式，适用于计算机视觉模型的训练和评估。