Patil Kailas, Gatagat Darshana, Rumane Omkar, Pashankar Siddharth, Chumchu Prawit
Vishwakarma University, Pune, India.
Kasetsart University, Sriracha, Thailand.
Data Brief. 2024 Aug 29;57:110887. doi: 10.1016/j.dib.2024.110887. eCollection 2024 Dec.
This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.
本文介绍了一个数据集，它包含16426张真实世界的城市照片，捕捉了早晨、傍晚和夜间场景中的车辆、骑自行车的人、摩托车和行人。该数据集对于交通分析、城市规划和公共安全中的机器学习任务具有重要价值。它有助于开发和验证行人检测、交通流分析和基础设施优化算法。我们的主要目标是协助学者、城市规划者和决策者创建用于行人安全、交通控制和事故预防的复杂模型。由于其高度的多样性和庞大的数量，该数据集是训练和验证旨在增强实时交通监测系统、优化城市基础设施以及整体道路安全的算法的有用资源。这个数据集代表了智慧城市项目和智能交通系统创建的一项重大进展。
