Spatial Extraction of Pavement Damage using Inspection Database with Car-mounted Cameras

Kachi, Kota; Asada, Takumi; Arimura, Mikiharu

doi:10.1109/gcce53005.2021.9621884

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The method of installing a camera on the vehicle and photographing the road at close range can obtain detailed road images. It is also low-cost and easy to operate, so it has been adopted by many studies [24,25]. However, the camera is often mounted in front of the vehicle and tilted downward to take images of the road.…”

Section: Introductionmentioning

confidence: 99%

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Jiang¹,

Gu²,

Yan³

2022

Meas. Sci. Technol.

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Rapid inspection of urban road cracks is vital to maintain traffic smoothness and ensure traffic safety. A rapid pavement crack inspection method using low-altitude aerial images captured by the unmanned aerial system (UAS) and deep-learning aided 3D reconstruction method, learning-based object segmentation algorithm is proposed to measure road cracks automatically. The contributions include: (1) An efficient 3D reconstruction method for low-altitude aerial images captured by UAS is proposed, which applies an instance segmentation network to segment road targets from raw images with complex backgrounds first and then perform structure from motion (SFM) to reconstruct a large-scale road orthophoto from a large number of aerial images. (2) To detect cracks from the reconstructed large-size road orthophoto, the sliding window algorithm and U-Net model optimized with transformer structure are used to automatically identify and segment the cracks from the orthophoto at a pixel level. Then the connected domain feature analysis method is used to measure the road crack length. The proposed method is applied to detect road cracks in a 1.5km2 area of a city. The results show that the proposed method can effectively and accurately detect cracks and measure the length of cracks in the 6.2km-long road, which proves the practicality of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Jiang¹,

Gu²,

Yan³

2022

Meas. Sci. Technol.

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“…In prior research, there have been efforts to identify road surface types using computer vision [3–9]. However, when using a vision sensor, it is difficult to properly define the road surface condition in situations such as blind spots or cloudy conditions.…”

Section: Introductionmentioning

confidence: 99%

Efficient Non‐Compression Auto‐Encoder for Driving Noise‐Based Road Surface Anomaly Detection

Park

Jung

2022

IEEJ Transactions Elec Engng

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Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared with the conventional models, and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection.

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Spatial Extraction of Pavement Damage using Inspection Database with Car-mounted Cameras

Cited by 2 publications

References 2 publications

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Efficient Non‐Compression Auto‐Encoder for Driving Noise‐Based Road Surface Anomaly Detection

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