Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway.
Sensors (Basel). 2023 Mar 8;23(6):2935. doi: 10.3390/s23062935.
Industry 4.0 has revolutionized the use of physical and digital systems while playing a vital role in the digitalization of maintenance plans for physical assets in an optimal way. Road network conditions and timely maintenance plans are essential in the predictive maintenance (PdM) of a road. We developed a PdM-based approach that uses pre-trained deep learning models to recognize and detect the road crack types effectively and efficiently. We, in this work, explore the use of deep neural networks to classify roads based on the amount of deterioration. This is done by training the network to identify various types of cracks, corrugation, upheaval, potholes, and other types of road damage. Based on the amount and severity of the damage, we can determine the degradation percentage and have a PdM framework where we can identify the intensity of damage occurrence and, thus, prioritize the maintenance decisions. The inspection authorities and stakeholders can make maintenance decisions for certain types of damages using our deep learning-based road predictive maintenance framework. We evaluated our approach using precision, recall, F1-score, intersection-over-union, structural similarity index, and mean average precision measures, and found that our proposed framework achieved significant performance.
工业 4.0 彻底改变了物理系统和数字系统的使用方式，在以最佳方式实现物理资产维护计划的数字化方面发挥了至关重要的作用。道路网络状况和及时的维护计划对于道路的预测性维护（PdM）至关重要。我们开发了一种基于 PdM 的方法，该方法使用预先训练的深度学习模型来有效地识别和检测道路裂缝类型。在这项工作中，我们探索了使用深度神经网络根据损坏程度对道路进行分类。这是通过训练网络来识别各种类型的裂缝、波纹、隆起、坑洼和其他类型的道路损坏来实现的。根据损坏的数量和严重程度，我们可以确定退化的百分比，并构建一个 PdM 框架，在该框架中，我们可以识别损坏发生的强度，从而确定维护决策的优先级。检查机构和利益相关者可以使用我们基于深度学习的道路预测性维护框架来为某些类型的损坏做出维护决策。我们使用精度、召回率、F1 分数、交并比、结构相似性指数和平均精度来评估我们的方法，发现我们提出的框架取得了显著的性能。
