Pseudo normal image generation for anomaly detection on road surface

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

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Road Surface Monitoring Using Smartphone Sensors: A Review

Sensors ◽

10.3390/s18113845 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3845 ◽

Cited By ~ 28

Author(s):

Shahram Sattar ◽

Songnian Li ◽

Michael Chapman

Keyword(s):

Anomaly Detection ◽

Surface Condition ◽

Low Frequency ◽

Road Surface ◽

Detection Accuracy ◽

Road Maintenance ◽

Key Factor ◽

Smartphone Sensors ◽

Road Surface Monitoring ◽

Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

Download Full-text

Normal Image Generation-Based Defect Detection by Generative Adversarial Network with Chaotic Random Images

Advances in Visual Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-90439-5_28 ◽

2021 ◽

pp. 353-365

Author(s):

Hiroki Kobayashi ◽

Ryo Miyoshi ◽

Manabu Hashimoto

Keyword(s):

Defect Detection ◽

Image Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Normal Image

Download Full-text

Road Surface Monitoring Using Smartphone Sensors: A Review

10.32920/14638491 ◽

2021 ◽

Author(s):

Shahram Sattar ◽

Songnian Li ◽

Michael A. Chapman

Keyword(s):

Anomaly Detection ◽

Surface Condition ◽

Low Frequency ◽

Road Surface ◽

Detection Accuracy ◽

Road Maintenance ◽

Key Factor ◽

Smartphone Sensors ◽

Road Surface Monitoring ◽

Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

Download Full-text