Capsule-Based Networks for Road Marking Extraction and Classification From Mobile LiDAR Point Clouds

Ma, Lingfei; Li, Ying; Li, Jonathan; Yu, Yongtao; Marcato, José; Gonçalves, Wesley Nunes; Chapman, Michael A.

doi:10.1109/tits.2020.2990120

Cited by 45 publications

(23 citation statements)

References 33 publications

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“…In routing by agreement, the outputs from one capsule are routed to capsules in the next layer according to the child capsule's ability to predict the parent capsule's outputs (Hinton et al, 2011). The squashing function, which is a non-linearity function, is combined by an additional scaling and a unit scaling (Ma et al, 2020):…”

Section: Proposed Modelmentioning

confidence: 99%

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Zhu

Tan

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.

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Section: Proposed Modelmentioning

confidence: 99%

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Zhu

Tan

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…3(a & b Index 18) , the abnormal distribution indictors (4,5,15,17) (shown in Fig. 3) were filtered out, and the similarity screening was based on formula (2) (3) (4) ,through these method the similarity index between indicators has taken, and group them with 95%(the similarity threshold) as (1,2,3), (4,5,7,9),( 6),( 8), (10,11,12),(13) (Fig. 4), then selected the highest quantitative distribution index as the representative of each group, finally through these steps 6 simplified indexes could be get.…”

Section: Analytic Hierarchy Process Modelmentioning

confidence: 99%

“…The equipment used in these works includes acceleration sensors [6][7] and image sensors [8], and the methods of classification in this routine include clustering [8], support vector machine [9], random forest [10]. The recognition of lane line defects includes two routes: laser point cloud [11][12] and image [14][15]. Laser point cloud can effectively identify lane line shape features [11], but marking's color information cannot be extracted and processed well in this way.…”

Section: Introductionmentioning

confidence: 99%

Network Optimization Based Lane Line Classification Study

Wang¹,

Deng²,

Sun³

et al. 2021

Preprint

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Efficient quality evaluation provides support for the timely and good maintenance of the lane line marking. This paper searches and optimizes the back propagation(BP) network model which referred to the analytic hierarchy process(AHP) model structure, as well as the number of nodes in the middle layer network. Based on this, a comprehensive evaluation method of multi-dimensional lane line quality such as shape, color and contrast is established. The experimental results show that the parameters of the model are more simplified, and the scoring and classification results of lane lines are more accurate.

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“…Road facility object detection using LiDAR data has been investigated extensively. (6)(7)(8)(9)(10) Hata and Wolf detected lanes by categorizing LiDAR point data for lanes and asphalt, (6) and Jo et al attempted to identify whether a traffic sign has disappeared or has been added. (7) Ma et al increased the accuracy of detecting and classifying road markings by applying a deep learning framework.…”

Section: Introductionmentioning

confidence: 99%

“…(7) Ma et al increased the accuracy of detecting and classifying road markings by applying a deep learning framework. (8) Pannen et al constructed a framework that recognizes changes by detecting lanes and immediately providing a crowdsourced updated HD map. (9) Kim et al used a point unit to determine whether shape change has occurred as well as to apply the change immediately; however, they did not specify the changed object.…”

Section: Introductionmentioning

confidence: 99%

Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates

Lee¹,

Jeong²,

Peter³

2022

Sensors and Materials

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Autonomous driving technology is significantly based on the fusion of high-definition (HD) maps and sensors. Therefore, the construction and update of HD maps must be emphasized to achieve full driving automation. Herein, a method is proposed to detect road facilities using object detection with images, particularly for HD map updates utilizing the You Only Look Once version 3 (YOLOv3) algorithm. The proposed approach, a deep-learning-based object detection method, utilizes transfer learning, which can detect objects in road facilities and record road sections that require maintenance. To test the effectiveness of the detection method, we analyze video footage captured in the Korean road environment. The experimental results show that this method achieves a mean average precision (mAP) of 58 and can update HD maps using a crowdsourcing framework.

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Capsule-Based Networks for Road Marking Extraction and Classification From Mobile LiDAR Point Clouds

Cited by 45 publications

References 33 publications

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

A Capsnets Approach to Pavement Crack Detection Using Mobile Laser Scannning Point Clouds

Network Optimization Based Lane Line Classification Study

Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates

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