FasterRCNN Monitoring of Road Damages: Competition and Deployment

• URL: http://arxiv.org/abs/2010.11780v1
• Date: Thu, 22 Oct 2020 14:56:00 GMT
• Title: FasterRCNN Monitoring of Road Damages: Competition and Deployment
• Authors: Hascoet Tristan, Yihao Zhang, Persch Andreas, Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki
• Abstract summary: The IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity for deep learning and computer vision researchers to get involved. 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.
• Score: 19.95568306575998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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