RSD-BD: Road Surface Damage Image Dataset from Major Cities of Bangladesh for Deep Learning & Computer Vision Research

This dataset comprises 1500 high-quality images depicting various forms of road surface damage collected from major cities in Bangladesh, specifically Dhaka, Mymensingh, and Chattogram. The dataset captures real-world conditions of urban and semi-urban road networks, providing valuable visual data for analysis in computer vision, machine learning, deep learning, and civil infrastructure research. The images are captured under diverse lighting conditions and angles, ensuring variability and practical utility for robust algorithm development. Dataset Composition: Total Images: 1500 Format: JPG Image Resolution: Varied, high-resolution suitable for computer vision tasks. Class-wise Distribution: Asphalt Damage: 500 images Crack: 500 images Pothole: 500 images Dataset Potential Applications: Training, validation, and benchmarking for deep learning and machine learning algorithms focusing on road infrastructure assessment. Development of computer vision-based automated systems for road damage detection and classification. Research and development in intelligent transportation systems (ITS), smart city infrastructure management, and predictive road maintenance. Analysis and testing of algorithms for damage severity assessment and automated cost estimation for repairs. Intended Users: Researchers in civil engineering, transportation, and urban planning. Machine learning and computer vision practitioners focus on infrastructure monitoring and predictive maintenance. Government bodies and policymakers are interested in infrastructural health assessments and proactive maintenance planning.本数据集包含1500张高质量图像，记录了孟加拉国达卡、迈门辛以及吉大港等主要城市中各类路面损坏情况。该数据集覆盖城市与半城市道路网络的真实场景，为计算机视觉、机器学习、深度学习以及民用基础设施研究领域的分析提供了极具价值的视觉数据。图像采集于多样的光照条件与拍摄角度，确保了数据的多样性与实用性，可用于开发鲁棒性更强的算法。 数据集构成： 总图像量：1500张 图像格式：JPG 图像分辨率：多样且为高分辨率，适配计算机视觉相关任务。 类别分布： 沥青路面损坏：500张 路面裂缝：500张 路面坑槽：500张 数据集潜在应用场景： 针对道路基础设施评估的深度学习与机器学习算法的训练、验证与基准测试。 基于计算机视觉的路面损坏检测与分类自动化系统开发。 智能交通系统（Intelligent Transportation Systems，ITS）、智慧城市基础设施管理以及预防性道路养护领域的研究与开发。 路面损坏严重程度评估算法以及维修成本自动估算算法的分析与测试。 目标用户群体： 土木工程、交通工程以及城市规划领域的研究人员。 专注于基础设施监测与预防性养护的机器学习与计算机视觉从业者。 关注基础设施健康评估与主动养护规划的政府机构与政策制定者。