Datasets:
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0 0.490000 0.468085 0.420000 0.553191 |
0 0.490000 0.468085 0.420000 0.553191 |
0 0.511765 0.455056 0.482353 0.573034 |
0 0.511765 0.455056 0.482353 0.573034 |
0 0.539062 0.487705 0.734375 0.745902 |
0 0.451613 0.506024 0.537634 0.602410 |
0 0.451613 0.506024 0.537634 0.602410 |
0 0.539062 0.487705 0.734375 0.745902 |
0 0.533058 0.490000 0.636364 0.840000 |
0 0.477901 0.482456 0.657459 0.672515 |
0 0.477901 0.482456 0.657459 0.672515 |
0 0.508333 0.511976 0.661111 0.700599 |
0 0.487500 0.514706 0.675000 0.852941 |
0 0.500000 0.509259 0.658537 0.796296 |
0 0.509615 0.484536 0.500000 0.577320 |
0 0.479452 0.506849 0.520548 0.575342 |
0 0.503937 0.500000 0.614173 0.719298 |
0 0.512987 0.528986 0.662338 0.710145 |
0 0.482394 0.488462 0.528169 0.669231 |
0 0.482394 0.488462 0.528169 0.669231 |
0 0.512346 0.500000 0.604938 0.791045 |
0 0.506250 0.486301 0.662500 0.753425 |
0 0.513441 0.494253 0.639785 0.816092 |
0 0.494624 0.503049 0.752688 0.823171 |
0 0.522152 0.503937 0.651899 0.771654 |
0 0.512903 0.496000 0.677419 0.784000 |
0 0.512903 0.496000 0.677419 0.784000 |
0 0.541667 0.481752 0.660256 0.744526 |
0 0.541667 0.481752 0.660256 0.744526 |
0 0.531915 0.494253 0.702128 0.735632 |
0 0.513975 0.513245 0.717391 0.867550 |
0 0.513975 0.513245 0.717391 0.867550 |
0 0.525943 0.500000 0.683962 0.855670 |
0 0.517460 0.498221 0.711111 0.832740 |
0 0.517460 0.498221 0.711111 0.832740 |
0 0.500000 0.516667 0.634615 0.722222 |
0 0.513889 0.494681 0.638889 0.712766 |
0 0.519417 0.500000 0.669903 0.733333 |
0 0.514563 0.494565 0.660194 0.706522 |
0 0.514563 0.494565 0.660194 0.706522 |
0 0.510417 0.510870 0.666667 0.717391 |
0 0.534884 0.511494 0.767442 0.724138 |
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0 0.519048 0.474747 0.714286 0.808081 |
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0 0.500000 0.492754 0.600000 0.695652 |
0 0.500000 0.492754 0.600000 0.695652 |
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0 0.500000 0.546154 0.569231 0.630769 |
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0 0.505882 0.520000 0.564706 0.640000 |
0 0.500000 0.516667 0.649123 0.733333 |
0 0.500000 0.516667 0.649123 0.733333 |
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0 0.491228 0.459184 0.526316 0.551020 |
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0 0.489286 0.524793 0.578571 0.685950 |
0 0.523041 0.489950 0.714286 0.758794 |
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0 0.494286 0.520000 0.668571 0.746667 |
0 0.506667 0.468750 0.706667 0.750000 |
0 0.506667 0.468750 0.706667 0.750000 |
0 0.513274 0.495050 0.637168 0.732673 |
0 0.513274 0.495050 0.637168 0.732673 |
0 0.513274 0.500000 0.637168 0.702970 |
0 0.513274 0.500000 0.637168 0.702970 |
0 0.511173 0.509494 0.586592 0.664557 |
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0 0.517699 0.500000 0.663717 0.742574 |
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0 0.508380 0.503165 0.603352 0.677215 |
0 0.508380 0.503165 0.603352 0.677215 |
0 0.513966 0.503165 0.592179 0.651899 |
0 0.504545 0.461165 0.536364 0.592233 |
0 0.506098 0.481707 0.548780 0.548780 |
0 0.506098 0.481707 0.548780 0.548780 |
0 0.739844 0.423611 0.043750 0.100000 |
0 0.739844 0.423611 0.043750 0.100000 |
0 0.739844 0.423611 0.043750 0.100000 |
0 0.739844 0.423611 0.043750 0.100000 |
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0 0.518657 0.484375 0.753731 0.859375 |
0 0.521212 0.496689 0.763636 0.834437 |
0 0.521212 0.496689 0.763636 0.834437 |
0 0.539062 0.487705 0.734375 0.745902 |
0 0.451613 0.506024 0.537634 0.602410 |
0 0.539062 0.487705 0.734375 0.745902 |
0 0.533058 0.490000 0.636364 0.840000 |
End of preview. Expand in Data Studio
CTRSDB: Chinese Traffic Regulatory Sign DataBase
数据集简介
CTRSDB是聚焦中国道路场景限速、禁行、让行三类核心管制交通标志的目标检测专用数据集,专为边缘端轻量级交通目标识别模型训练优化,覆盖阴天、雨雾、信号干扰等真实复杂道路场景,完美适配YOLO系列等主流检测模型。
核心亮点
- 场景针对性强:聚焦自动驾驶、辅助驾驶最核心的管制类交通标志,无冗余类别,标注精度高
- 恶劣场景适配:通过AI生成扩增了雨雾极端天气低能见度场景数据,提升模型在复杂天气下的鲁棒性
- 开箱即用:原生支持YOLO格式标注,配套训练配置文件,clone后可直接用于模型训练
- 合规开源:遵循CC BY-NC-SA 4.0协议,仅用于学术学习与非商用场景
数据集详情
| 项目 | 详情 |
|---|---|
| 总图片数量 | 3960张 |
| 核心类别 | 限速、禁行通行、禁止驶入、减速让行、停车让行 |
| 标注格式 | YOLO原生txt格式 |
| 数据集划分 | 训练集:验证集:测试集=7:2:1 |
数据集中英名对照
| 类别 | 中文名称 | 英文名称 |
|---|---|---|
| 0 | 限速5公里/小时 | Speed Limit 5km/h |
| 1 | 限速15公里/小时 | Speed Limit 15km/h |
| 2 | 限速30公里/小时 | Speed Limit 30km/h |
| 3 | 限速40公里/小时 | Speed Limit 40km/h |
| 4 | 限速50公里/小时 | Speed Limit 50km/h |
| 5 | 限速60公里/小时 | Speed Limit 60km/h |
| 6 | 限速70公里/小时 | Speed Limit 70km/h |
| 7 | 限速80公里/小时 | Speed Limit 80km/h |
| 8 | 停车让行 | Stop |
| 9 | 禁止通行 | Road Closed |
| 10 | 禁止驶入 | No Entry |
| 11 | 减速让行 | Give Way |
数据来源与合规说明
本数据集严格遵循开源协议规范,所有内容均合规可追溯:
- 基础数据融合自以下公开数据集,使用完全遵循原数据集的开源协议:
- 新增自主采集:重点补充采集了禁止通行这一细分种类数据集
- 数据扩增处理:实施AI雨雾效果、亮度扰动、模拟信号干扰等数据扩增手段,扩充训练样本的多样性。
使用方法
# Hugging Face datasets库直接加载
from datasets import load_dataset
dataset = load_dataset("EpicZhang/ChineseTrafficRegulatorySignDataBase")
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