Advanced pavement distress recognition and 3D reconstruction by using GA-DenseNet and binocular stereo vision

Li, Jiale; Liu, Tao; Wang, Xuefei

doi:10.1016/j.measurement.2022.111760

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…Over the years, three-dimensional (3D) methods utilizing laser scanning, ground penetrating radar (GPR), and unmanned aerial vehicles (UAVs) have been a hot spot for detecting 3D pavement characteristics like volume and depth. PaveVision3D, Pavemetrics, and RICOH are typical automated detection systems that apply 3D techniques [12,13]. For example, PaveVision3D can conduct a complete lane width distress detection survey at 1 mm resolution at a speed up to 100 KM/h [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

Wang,

Qi,

Sun

et al. 2024

Applied Sciences

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With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

Wang,

Qi,

Sun

et al. 2024

Applied Sciences

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“…Binocular stereo vision represents a prominent domain within 3D virtual vision, boasting extensive applications across various fields such as 3D measurement, autonomous vehicle navigation, 3D reconstruction, and target tracking ( Jiang, 2022 ). The foundation of binocular stereo-vision technology encompasses crucial components such as camera calibration and 3D reconstruction, with the stereo-matching algorithm serving as the paramount stage within this comprehensive framework ( Li, Liu & Wang, 2022 ). Consequently, the quest for precise and efficient stereo-matching algorithms enables the establishing of a robust binocular stereo-vision system and propels the advancement of 3D virtual vision.…”

Section: Introductionmentioning

confidence: 99%

Research on 3D virtual vision matching based on interactive color segmentation

Wang,

Jin

et al. 2024

PeerJ Computer Science

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Given the prevalent issues surrounding accuracy and efficiency in contemporary stereo-matching algorithms, this research introduces an innovative image segmentation-based approach. The proposed methodology integrates residual and Swim Transformer modules into the established 3D Unet framework, yielding the Res-Swim-UNet image segmentation model. The algorithm estimates the disparateness of segmented outputs by employing regression techniques, culminating in a comprehensive disparity map. Experimental findings underscore the superiority of the proposed algorithm across all evaluated metrics. Specifically, the proposed network demonstrates marked improvements, with IoU and mPA enhancements of 2.9% and 162%, respectively. Notably, the average matching error rate of the algorithm registers at 2.02%, underscoring its efficacy in achieving precise stereoscopic matching. Moreover, the model’s enhanced generalization capability and robustness underscore its potential for widespread applicability.

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“…In addition to the above methods, Li et al fused genetic algorithms with visual sensing to achieve the 3D reconstruction and extraction of the morphological features of potholes through point cloud processing [15]. Notably, Zhang et al used an unmanned aerial vehicle (UAV) road-damage database and described a multi-level attention mechanism called multilevel attention block (MLAB) to enhance the use of basic features using YOLO v3 (You Only Look Once version 3) [16].…”

Section: Introductionmentioning

confidence: 99%

VIDAR-Based Road-Surface-Pothole-Detection Method

Xu,

Sun,

Ding

et al. 2023

Sensors

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This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By comparing it with the classical method and using the confusion matrix to judge the correctness, recall and accuracy of the method proposed in this paper, it is verified that the method proposed in this paper can improve the accuracy of monocular vision in detecting potholes in road surfaces.

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Advanced pavement distress recognition and 3D reconstruction by using GA-DenseNet and binocular stereo vision

Cited by 16 publications

References 46 publications

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

Research on 3D virtual vision matching based on interactive color segmentation

VIDAR-Based Road-Surface-Pothole-Detection Method

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