Pavement Crack Recognition Based on Aerial Image

Bo, 王 博 Wang; Xia, 王霞 Wang; Fei, 陈飞 Chen; Yuntao, 贺云涛 He; Wenguang, 李文光 Li; Li, 刘 莉 Liu

doi:10.3788/aos201737.0810004

Cited by 5 publications

(2 citation statements)

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“…e existing researches [10,11,16,18] usually identify road distress according to fixed initial parameters obtained by external measurements. However, the approach used is not feasible for automatic applications and the method ignores the possibility of multiple surfaces.…”

Section: Roadway Reference Planementioning

confidence: 99%

“…e collection of pavement data through special equipment and recent advances in high-performance computing have paved the way for crack detection algorithms. For example, Bo et al [10] used a morphological filtering algorithm to detect road surface diseases, and [11] proposed a local binary algorithm. However, recently, machine learning algorithms with higher accuracy and better feature extraction capabilities have been applied to pavement disease detection.…”

Section: Introductionmentioning

confidence: 99%

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A Depth Camera‐Based Intelligent Method for Identifying and Quantifying Pavement Diseases

Bai¹,

Chen³

et al. 2022

Advances in Civil Engineering

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In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test the label database. The method does not require a predetermined distance between the measurement target and the sensor; can be applied to moving scenes; and is important for the detection, classification, and quantification of pavement diseases. The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy.

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Section: Roadway Reference Planementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%