FasterRCNN Monitoring of Road Damages: Competition and Deployment
FasterRCNN Monitoring of Road Damages: Competition and Deployment
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DOI：
10.1109/bigdata50022.2020.9377871
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
T. Hascoet;Yihao Zhang;Andreas Persch;R. Takashima;T. Takiguchi;Y. Ariki
中科院分区：
文献类型：
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作者：
T. Hascoet;Yihao Zhang;Andreas Persch;R. Takashima;T. Takiguchi;Y. Ariki
维护老化的基础设施是目前世界各地地方和国家管理者面临的一个挑战。有效的基础设施维护的一个重要先决条件是持续监测（即量化安全性和可靠性水平）超大型结构的状态。与此同时，计算机视觉近年来取得了令人印象深刻的进步，这主要是由于深度学习模型的成功应用。这些新颖的进展允许视觉任务的自动化，这在以前是不可能自动化的，为帮助管理员优化他们的基础设施维护操作提供了有希望的可能性。在这种背景下，IEEE 2020全球道路损伤检测（RDD）挑战赛为深度学习和计算机视觉研究人员提供了一个参与并帮助准确跟踪道路网络路面损伤的机会。本文对该主题提出了两个贡献：在第一部分中，我们详细介绍了RDD挑战的解决方案。在第二部分中，我们介绍了在本地道路网络上部署我们的模型的努力，解释了提出的方法和遇到的挑战。
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides in recent years, mainly due to successful applications of deep learning models. These novel progresses are allowing the automation of vision tasks, which were previously impossible to automate, offering promising possibilities to assist administrators in optimizing their infrastructure maintenance operations. In this context, the IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity for deep learning and computer vision researchers to get involved and help accurately track pavement damages on road networks. This paper proposes two contributions to that topic: In a first part, we detail our solution to the RDD Challenge. In a second part, we present our efforts in deploying our model on a local road network, explaining the proposed methodology and encountered challenges.