Pavement Damage Crack Recognition Method Based on High-Resolution Linear Array Cameras and Adaptive Lifting Algorithm

Han, Yingjie; Tu, Shu‐Jiang; Yang, Yilin; K, Lei; C, Liu

doi:10.20944/preprints201802.0102.v1

Cited by 1 publication

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome such problems and obtain high-resolution data, sensors have been integrated to road vehicles platforms. Such platforms can contain instruments such as LiDAR [32,33], laser scanners [34][35][36] and photo or video cameras [37][38][39], GPR [40][41][42][43][44] to identify and calculate distress parameters. An improvement of automatic or semi-automatic distress characterization can be also reached by using Unmanned Ground Vehicles (UGVs).…”

Section: Introductionmentioning

confidence: 99%

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

Mei

Zampetti

Mascio

et al. 2022

Sensors

View full text Add to dashboard Cite

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the IROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as “Good”/“Preventive Maintenance”, “Fair”/“Rehabilitation”, “Poor”/“Reconstruction”, which are ranges of the custom PCI ranting scale and represents a typical repair strategy.

show abstract