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Low-Cost Sensors for Road Condition Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 10300

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Prof. Dr. Giuseppe Loprencipe

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DICEA, Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy
Interests: road structure health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Road networks are monitored to evaluate their decay level and performance regarding ride comfort, vehicle rolling noise, fuel consumption, etc. Various sensors and methods to analyze road conditions can be used, but most of them are expensive, time-consuming, and labor-intensive work. Recently, many low-cost sensors have been proposed for road condition monitoring with different characteristics and performances. Additionally, smartphone apps have been implemented so that many road managers can begin maintenance activities without a large outlay of economic resources. The assessment and handling of the different characteristics of different sensors represent an interesting challenge for road condition monitoring. This Special Issue will gather studies on modern technologies that can modernize our infrastructures and transform them into smart roads, investigating the limitations and the potential of these technologies in the civil engineering field.

Prof. Dr. Giuseppe Loprencipe
Guest Editor

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Published Papers (6 papers)

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Research

19 pages, 64842 KiB

Open AccessArticle

Detection of Road Potholes by Applying Convolutional Neural Network Method Based on Road Vibration Data

by Furkan Ozoglu and Türkay Gökgöz

Sensors 2023, 23(22), 9023; https://doi.org/10.3390/s23229023 - 07 Nov 2023

Viewed by 2162

Abstract

In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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28 pages, 8409 KiB

Open AccessArticle

Traffic Stream Analysis by Radar Sensors on Two-Lane Roads for Free-Moving and Constrained Vehicles Identification

by Giuseppe Cantisani, Giulia Del Serrone, Raffaele Mauro, Paolo Peluso and Andrea Pompigna

Sensors 2023, 23(15), 6922; https://doi.org/10.3390/s23156922 - 03 Aug 2023

Cited by 1 | Viewed by 1190

Abstract

This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data. This study introduces an exponential headway model to identify the headway threshold above which vehicles can be considered as unconditioned. However, this value could identify vehicles which still retain some autonomy in their speed and maneuvering. Therefore, an additional criterion to distinguish between apparently and actually conditioned vehicles has been introduced, analyzing the speed differences between a vehicle and the preceding one. Three-month sequences of traffic monitored through radar devices placed on some Italian two-lane roads have been analyzed and an exponential headway model has been introduced, as an illustrative example. The results show that introducing the criterion of maneuvering freedom can significantly improve traffic flow analysis, modifying the starting critical values of 4 and 8 s per each studied section, to 2.5 and 3 s, approaching the values suggested by international manuals for traffic flow quality analysis. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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16 pages, 5021 KiB

Open AccessArticle

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping

by Salvatore Bruno, Giuseppe Loprencipe, Paola Di Mascio, Giuseppe Cantisani, Nicola Fiore, Carlo Polidori, Antonio D’Andrea and Laura Moretti

Sensors 2023, 23(13), 5860; https://doi.org/10.3390/s23135860 - 24 Jun 2023

Cited by 1 | Viewed by 1580

Abstract

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole’s shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers’ exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers’ and road users’ health and safety. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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15 pages, 16290 KiB

Open AccessArticle

Pavement Roughness Grade Recognition Based on One-dimensional Residual Convolutional Neural Network

by Juncai Xu and Xiong Yu

Sensors 2023, 23(4), 2271; https://doi.org/10.3390/s23042271 - 17 Feb 2023

Cited by 3 | Viewed by 1257

Abstract

A pavement’s roughness seriously affects its service life and driving comfort. Considering the complexity and low accuracy of the current recognition algorithms for the roughness grade of pavements, this paper proposes a real-time pavement roughness recognition method with a lightweight residual convolutional network and time-series acceleration. Firstly, a random input pavement model is established by the white noise method, and the pavement roughness of a 1/4 vehicle vibration model is simulated to obtain the vehicle vibration response data. Then, the residual convolutional network is used to learn the deep-level information of the sample signal. The residual convolutional neural network recognizes the pavement roughness grade quickly and accurately. The experimental results show that the residual convolutional neural network has a robust feature-capturing ability for vehicle vibration signals, and the classification features can be obtained quickly. The accuracy of pavement roughness classification is as high as 98.7%, which significantly improves the accuracy and reduces the computational effort of the recognition algorithm, and is suitable for pavement roughness grade classification. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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17 pages, 4578 KiB

Open AccessArticle

Detection and Recognition Algorithm of Arbitrary-Oriented Oil Replenishment Target in Remote Sensing Image

by Yongjie Hou, Qingwen Yang, Li Li and Gang Shi

Sensors 2023, 23(2), 767; https://doi.org/10.3390/s23020767 - 09 Jan 2023

Cited by 2 | Viewed by 1243

Abstract

In view of the fact that the aerial images of UAVs are usually taken from a top-down perspective, there are large changes in spatial resolution and small targets to be detected, and the detection method of natural scenes is not effective in detecting under the arbitrary arrangement of remote sensing image direction, which is difficult to apply to the detection demand scenario of road technology status assessment, this paper proposes a lightweight network architecture algorithm based on MobileNetv3-YOLOv5s (MR-YOLO). First, the MobileNetv3 structure is introduced to replace part of the backbone network of YOLOv5s for feature extraction so as to reduce the network model size and computation and improve the detection speed of the target; meanwhile, the CSPNet cross-stage local network is introduced to ensure the accuracy while reducing the computation. The focal loss function is improved to improve the localization accuracy while increasing the speed of the bounding box regression. Finally, by improving the YOLOv5 target detection network from the prior frame design and the bounding box regression formula, the rotation angle method is added to make it suitable for the detection demand scenario of road technology status assessment. After a large number of algorithm comparisons and data ablation experiments, the feasibility of the algorithm was verified on the Xinjiang Altay highway dataset, and the accuracy of the MR-YOLO algorithm was as high as 91.1%, the average accuracy was as high as 92.4%, and the detection speed reached 96.8 FPS. Compared with YOLOv5s, the p-value and mAP values of the proposed algorithm were effectively improved. It can be seen that the proposed algorithm improves the detection accuracy and detection speed while greatly reducing the number of model parameters and computation. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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20 pages, 12128 KiB

Open AccessArticle

Development of a GIS-Based Methodology for the Management of Stone Pavements Using Low-Cost Sensors

by Salvatore Bruno, Lorenzo Vita and Giuseppe Loprencipe

Sensors 2022, 22(17), 6560; https://doi.org/10.3390/s22176560 - 31 Aug 2022

Cited by 9 | Viewed by 2050

Abstract

Stone pavements are present in many cities and their historical and cultural importance is well recognized. However, there are no standard monitoring methods for this type of pavement that allow road managers to define appropriate maintenance strategies. In this study, a novel method is proposed in order to monitor the road surface conditions of stone pavements in a quick and easy way. Field tests were carried out in an Italian historic center using accelerometer sensors mounted on both a car and a bicycle. A post-processing phase of that data defined the comfort perception of the road users in terms of the a_wz index, as described in the ISO 2631 standard. The results derived from the dynamic surveys were also compared with the corresponding values of typical pavement indicators such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), measured only on a limited portion of the urban road network. The network’s implementation in a Geographic Information System (GIS) represents the surveys’ results in a graphical database. The specifications of the adopted method require that the network is divided into homogeneous sections, useful for measurement campaign planning, and adopted for the GIS’ outputs representation. The comparisons between IRI-a_wz (R² = 0.74) and PCI-a_wz (R² = 0.96) confirmed that the proposed method can be used reliably to assess the stone pavement conditions on the whole urban road network. Full article

(This article belongs to the Special Issue Low-Cost Sensors for Road Condition Monitoring)

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