The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

中文翻译：

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利用深度学习技术从超声波数据中检测轨道指示，以实现自动轨道监控和维护

使用铁路的乘客和服务数量的不断增加以及铁路使用量的相应增加导致了钢轨磨损和表面缺陷的加速，这使得钢轨缺陷识别成为铁路维护和监测以确保安全高效运行的重要问题。用于识别钢轨缺陷的传统目视检查方法非常耗时、准确度较低，并且容易出现人为错误。深度学习已用于改进铁路维护和监控任务。本研究旨在通过使用超声波图像数据比较不同的深度学习模型，开发一种用于检测铁路伪影和缺陷的结构化模型。这项研究表明使用图像分类和超声波数据中的目标检测技术来识别轨道指示是否实用，以及上述方法中哪种模型表现更好。该方法包括数据处理、标记以及使用不同的传统神经网络来开发图像分类和对象检测模型。用于图像分类的 CNN 和用于目标检测的 YOLOv5 的结果分别显示出 98% 和 99% 的准确率。这些模型可以在现实场景中高效、准确地识别铁路工件，从而改善铁路基础设施的自动化监控和维护。