Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data

Balado, Jesús; Martínez-Sánchez, J.; Arias, Pedro; Novo, Alexandre

doi:10.3390/s19163466

Cited by 81 publications

(42 citation statements)

References 51 publications

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“…To improve computing efficiency and keep spatial information, some deep learning models based on raw point cloud are proposed. PointNet [25] is a pioneering network architecture that works on raw point cloud and has been used for 3D object recognition [26][27][28]. It improves the performance of point cloud classification and segmentation.…”

Section: Related Workmentioning

confidence: 99%

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Lin

Chen

et al. 2020

Sensors

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High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.

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Section: Related Workmentioning

confidence: 99%

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Lin

Chen

et al. 2020

Sensors

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“…Then, the class label of the feature vector is obtained using a classifier such as Support Vector Machine (SVM) or with Deep Learning-based (DL) methods [42][43][44]. Among the latter, Convolutional Neural Networks (CNN) have been widely adopted, given their high performance in both TSD and TSR in images [45][46][47][48] and in point clouds [49].…”

Section: Related Workmentioning

confidence: 99%

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

et al. 2020

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Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

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“…In this field, not only MLS but also ALS data are used to test the new convolutional networks used for point cloud classification. Balado et al [25] use ALS for the automatic classification of land cover using the classifier ResNet-50, and in a later work, proved the validity of PointNet for MLS point clouds' classification [26]. Although many authors are working to improve these methods [27,28], they still need to evolve.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds

et al. 2019

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Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed.

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Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data

Cited by 81 publications

References 51 publications

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds

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