Gosiewska Alicja, Baran Zuzanna, Baran Monika, Rutkowski Tomasz
Nevomo IoT, 03-828 Warsaw, Poland.
Sensors (Basel). 2023 Sep 9;23(18):7776. doi: 10.3390/s23187776.
Railway infrastructure monitoring is crucial for transportation reliability and travelers' safety. However, it requires plenty of human resources that generate high costs and is limited to the efficiency of the human eye. Integrating machine learning into the railway monitoring process can overcome these problems. Since advanced algorithms perform equally to humans in many tasks, they can provide a faster, cost-effective, and reproducible evaluation of the infrastructure. The main issue with this approach is that training machine learning models involves acquiring a large amount of labeled data, which is unavailable for rail infrastructure. We trained YOLOv5 and MobileNet architectures to meet this challenge in low-data-volume scenarios. We established that 120 observations are enough to train an accurate model for the object-detection task for railway infrastructure. Moreover, we proposed a novel method for extracting background images from railway images. To test our method, we compared the performance of YOLOv5 and MobileNet on small datasets with and without background extraction. The results of the experiments show that background extraction reduces the sufficient data volume to 90 observations.
铁路基础设施监测对于运输可靠性和旅客安全至关重要。然而，这需要大量人力资源，成本高昂，且受限于人眼的效率。将机器学习集成到铁路监测过程中可以克服这些问题。由于先进算法在许多任务中表现与人类相当，它们可以对基础设施进行更快、更具成本效益且可重复的评估。这种方法的主要问题在于训练机器学习模型需要获取大量带标签的数据，而铁路基础设施方面无法获得此类数据。我们训练了YOLOv5和MobileNet架构以应对低数据量场景下的这一挑战。我们确定120次观测足以训练出用于铁路基础设施目标检测任务的精确模型。此外，我们提出了一种从铁路图像中提取背景图像的新方法。为测试我们的方法，我们比较了有无背景提取情况下YOLOv5和MobileNet在小数据集上的性能。实验结果表明，背景提取将所需数据量减少至90次观测。
