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

2020 ◽  
Vol 12 (3) ◽  
pp. 442 ◽  
Author(s):  
Jesús Balado ◽  
Elena González ◽  
Pedro Arias ◽  
David Castro
Keyword(s):  
False Positive Rate ◽  
Good Condition ◽  
Point Clouds ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Novel Approach ◽  
Mapping System ◽  
Positive Rate ◽  
Mobile Mapping System

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.

scholarly journals LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 661-668 ◽  
Author(s):  
Y. Li ◽  
J. Fan ◽  
Y. Huang ◽  
Z. Chen
Keyword(s):  
Geometric Constraints ◽  
Transportation Infrastructure ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Sign Detection ◽  
Mobile Mapping System ◽  
Transportation Agencies ◽  
Traffic Sign Detection

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 661-668
Author(s):  
Y. Li ◽  
J. Fan ◽  
Y. Huang ◽  
Z. Chen
Keyword(s):  
Geometric Constraints ◽  
Transportation Infrastructure ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Sign Detection ◽  
Mobile Mapping System ◽  
Transportation Agencies ◽  
Traffic Sign Detection

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals INVESTIGATING THE PERFORMANCE OF A HANDHELD MOBILE MAPPING SYSTEM IN DIFFERENT OUTDOOR SCENARIOS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 103-109
Author(s):  
E. Maset ◽  
S. Cucchiaro ◽  
F. Cazorzi ◽  
F. Crosilla ◽  
A. Fusiello ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Qualitative Assessment ◽  
Case Scenario ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Survey Instruments ◽  
Large Building

Abstract. In recent years, portable Mobile Mapping Systems (MMSs) are emerging as valuable survey instruments for fast and efficient mapping of both internal and external environments. The aim of this work is to assess the performance of a commercial handheld MMS, Gexcel HERON Lite, in two different outdoor applications. The first is the mapping of a large building, which represents a standard use-case scenario of this technology. Through the second case study, that consists in the survey of a torrent reach, we investigate instead the applicability of the handheld MMS for natural environment monitoring, a field in which portable systems are not yet widely employed. Quantitative and qualitative assessment is presented, comparing the point clouds obtained from the HERON Lite system against reference models provided by traditional techniques (i.e., Terrestrial Laser Scanning and Photogrammetry).

Pothole Mapping and Patching Quantity Estimates using LiDAR-Based Mobile Mapping Systems

2020 ◽  
Vol 2674 (9) ◽  
pp. 124-134
Author(s):  
Radhika Ravi ◽  
Ayman Habib ◽  
Darcy Bullock
Keyword(s):  
Point Clouds ◽  
Relative Accuracy ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Pavement Distress ◽  
The Road ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The Cost ◽  
Mapping Systems

Pavement distress or pothole mapping is important to public agencies responsible for maintaining roadways. The efficient capture of 3D point cloud data using mapping systems equipped with LiDAR eliminates the time-consuming and labor-intensive manual classification and quantity estimates. This paper proposes a methodology to map potholes along the road surface using ultra-high accuracy LiDAR units onboard a wheel-based mobile mapping system. LiDAR point clouds are processed to detect and report the location and severity of potholes by identifying the below-road 3D points pertaining to potholes, along with their depths. The surface area and volume of each detected pothole is also estimated along with the volume of its minimum bounding box to serve as an aide to choose the ideal method of repair as well as to estimate the cost of repair. The proposed approach was tested on a 10 mi-long segment on a U.S. Highway and it is observed to accurately detect potholes with varying severity and different causes. A sample of potholes detected in a 1 mi segment has been reported in the experimental results of this paper. The point clouds generated using the system are observed to have a single-track relative accuracy of less than ±1 cm and a multi-track relative accuracy of ±1–2 cm, which has been verified through comparing point clouds captured by different sensors from different tracks.

On the applicability of a scan-based mobile mapping system for monitoring the planarity and subsidence of road surfaces – Pilot study on the A44n motorway in Germany

2020 ◽  
Vol 14 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Erik Heinz ◽  
Christian Eling ◽  
Lasse Klingbeil ◽  
Heiner Kuhlmann
Keyword(s):  
Standard Deviation ◽  
Point Clouds ◽  
Road Surface ◽  
Control Points ◽  
Mobile Mapping ◽  
The Road ◽  
Mapping System ◽  
Road Surfaces ◽  
Mobile Mapping System ◽  
Physical Height

AbstractKinematic laser scanning is widely used for the fast and accurate acquisition of road corridors. In this context, road monitoring is a crucial application, since deficiencies of the road surface due to non-planarity and subsidence put traffic at risk. In recent years, a Mobile Mapping System (MMS) has been developed at the University of Bonn, consisting of a GNSS/IMU unit and a 2D laser scanner. The goal of this paper is to evaluate the accuracy and precision of this MMS, where the height component is of main interest. Following this, the applicability of the MMS for monitoring the planarity and subsidence of road surfaces is analyzed. The test area for this study is a 6 km long section of the A44n motorway in Germany. For the evaluation of the MMS, leveled control points along the motorway as well as point cloud comparisons of repeated passes were used. In order to transform the ellipsoidal heights of the MMS into the physical height system of the control points, undulations were utilized. In this respect, a local tilt correction for the geoid model was determined based on GNSS baselines and leveling, leading to a physical height accuracy of the MMS of < 10 mm (RMS). The related height precision has a standard deviation of about 5 mm. Hence, a potential subsidence of the road surface in the order of a few cm is detectable. In addition, the point clouds were used to analyze the planarity of the road surface. In the course of this, the cross fall of the road was estimated with a standard deviation of < 0.07 %. Yet, no deficiencies of the road surface in the form of significant rut depths or fictive water depths were detected, indicating the proper condition of the A44n motorway. According to our tests, the MMS is appropriate for road monitoring.

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