交通目标检测数据集.zip

上传者: wcl291121957 | 上传时间: 2025-09-23 11:08:18 | 文件大小: 5.15MB | 文件类型: ZIP
一、基础信息 数据集名称:交通目标检测数据集 图片数量: - 训练集:64张图片 - 验证集:21张图片 - 测试集:18张图片 总计:103张交通场景图片 核心类别: - 车辆类型:Car, Bike, Scooter, Truck, Bus, Motorcycle, Autorickshaw - 行人:Pedestrian, Adult, Child, Rider - 交通设施:Traffic Light, Traffic Sign, Crosswalk, Bus Stop - 交通标志:Speed Limit 20 KMPh, Stop, Do Not Enter, Parking (共49个精细类别,覆盖交通场景核心要素) 标注格式: YOLO格式,包含目标边界框坐标及类别索引 数据特性: 真实道路场景图像,包含日间、夜间及多种天气条件 二、适用场景 自动驾驶感知系统开发: 训练车辆、行人、交通信号灯的实时检测模型,为自动驾驶决策提供环境感知基础 智能交通监控系统: 部署于道路监控场景,实现车流统计、违规行为识别(如闯红灯、违规停车) 交通基础设施管理: 自动识别道路标志牌状态、斑马线位置等基础设施,辅助智慧城市建设 车载安全辅助系统: 集成至ADAS系统,提供前方碰撞预警、行人检测等安全功能 三、数据集优势 多维度场景覆盖: 包含车辆(前/后视角)、行人(成人/儿童)、两轮车、特殊车辆等49类目标,覆盖复杂交通要素 真实道路数据: 采集自真实驾驶场景,包含城市道路、交叉路口等环境,提供贴近实际应用的样本 精细标注体系: - 区分车辆具体类型(卡车/巴士/三轮车等) - 细分交通信号灯状态(红/黄/绿灯) - 包含特殊交通标识(禁止转向/限速等) 即用性设计: YOLO格式标注兼容主流检测框架(YOLOv5/v8, Detectron2等),开箱即用 场景鲁棒性: 包含雨天、夜间等挑战性场景数据,增强模型环境适应能力

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