FasterRCNN Monitoring of Road Damages: Competition and Deployment
| Autor: | Yasuo Ariki, Yihao Zhang, Tristan Hascoet, Tetsuya Takiguchi, Ryoichi Takashima, Andreas Persch |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Reliability (computer networking) Deep learning Computer Science - Computer Vision and Pattern Recognition Context (language use) 02 engineering and technology 010501 environmental sciences Track (rail transport) 01 natural sciences Maintenance engineering Automation 020901 industrial engineering & automation Risk analysis (engineering) Software deployment Damages Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | IEEE BigData |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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