Arain Asfar, Mehran Sanaullah, Shaikh Muhammad Zakir, Kumar Dileep, Chowdhry Bhawani Shankar, Hussain Tanweer
NCRA MUET, NCRA Condition Monitoring Systems Lab, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.
Departmento de Ingeniería de Mecánica y Eficiencia Energética, Universidad de Malaga, 29016 Malaga, Spain.
Data Brief. 2024 Jan 9;52:110050. doi: 10.1016/j.dib.2024.110050. eCollection 2024 Feb.
Railway infrastructure maintenance is critical for ensuring safe and efficient transportation networks. Railway track surface defects such as cracks, flakings, joints, spallings, shellings, squats, grooves pose substantial challenges to the integrity and longevity of the tracks. To address these challenges and facilitate further research, a novel dataset of railway track surface faults has been presented in this paper. It is collected using the EKENH9R cameras mounted on a railway inspection vehicle. This dataset represents a valuable resource for the railway maintenance and computer vision related scientific communities. This dataset includes a diverse range of real-world track surface faults under various environmental conditions and lighting scenarios. This makes it an important asset for the development and evaluation of Machine Learning (ML), Deep Learning (DL), and image processing algorithms. This paper also provides detailed annotations and metadata for each image class, enabling precise fault classification and severity assessment of the defects. Furthermore, this paper discusses the data collection process, highlights the significance of railway track maintenance, emphasizes the potential applications of this dataset in fault identification and predictive maintenance, and development of automated inspection systems. We encourage the research community to utilize this dataset for advancing the state-of-the-art research related to railway track surface condition monitoring.
铁路基础设施维护对于确保安全高效的运输网络至关重要。铁路轨道表面缺陷，如裂缝、剥落、接头、散裂、脱壳、凹陷、凹槽等，对轨道的完整性和使用寿命构成了重大挑战。为应对这些挑战并促进进一步研究，本文提出了一个新颖的铁路轨道表面故障数据集。它是使用安装在铁路检查车辆上的EKENH9R相机收集的。该数据集对于铁路维护和计算机视觉相关科学界而言是一项宝贵资源。该数据集包括在各种环境条件和光照场景下的各种真实世界轨道表面故障。这使其成为机器学习（ML）、深度学习（DL）和图像处理算法开发与评估的重要资产。本文还为每个图像类别提供了详细的注释和元数据，从而能够对缺陷进行精确的故障分类和严重程度评估。此外，本文讨论了数据收集过程，强调了铁路轨道维护的重要性，强调了该数据集在故障识别和预测性维护以及自动检测系统开发中的潜在应用。我们鼓励研究界利用该数据集推进与铁路轨道表面状况监测相关的前沿研究。
