466 research outputs found
CommuniSense: Crowdsourcing Road Hazards in Nairobi
Nairobi is one of the fastest growing metropolitan cities and a major
business and technology powerhouse in Africa. However, Nairobi currently lacks
monitoring technologies to obtain reliable data on traffic and road
infrastructure conditions. In this paper, we investigate the use of mobile
crowdsourcing as means to gather and document Nairobi's road quality
information. We first present the key findings of a city-wide road quality
survey about the perception of existing road quality conditions in Nairobi.
Based on the survey's findings, we then developed a mobile crowdsourcing
application, called CommuniSense, to collect road quality data. The application
serves as a tool for users to locate, describe, and photograph road hazards. We
tested our application through a two-week field study amongst 30 participants
to document various forms of road hazards from different areas in Nairobi. To
verify the authenticity of user-contributed reports from our field study, we
proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to
verify whether submitted reports indeed depict road hazards. We found 92% of
user-submitted reports to match the MTurkers judgements. While our prototype
was designed and tested on a specific city, our methodology is applicable to
other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer
Interaction with Mobile Devices and Services (MobileHCI 2015
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Influence of Vehicle Make on Accuracy of Real-time Road Anomaly Identification
As road infrastructure in the United States is aging, road anomalies such as cracks, potholes, and other abnormalities are becoming much more prevalent. Currently there is no real-time understanding of the conditions of roads, thus we developed a machine-learning algorithm developed and trained to identify road conditions in real time based on data collected by smartphones. Since there are a multitude of different vehicles on the roads and locations where phones can be placed in the vehicle, creating a classification algorithm that can work regardless of the vehicle type and phone placement is incredibly important. Doing a comparative study on the different vibrations received at different locations in different vehicles will provide a baseline for future development of a universal algorithm that uses crowd sourced data from cell phones to allow for real-time awareness of changing road conditions. This in turn provides a way to identify and fix dangerous road anomalies quickly
Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data
ABSTRACTThis paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the parti..
RDD2022: A multi-national image dataset for automatic Road Damage Detection
The data article describes the Road Damage Dataset, RDD2022, which comprises
47,420 road images from six countries, Japan, India, the Czech Republic,
Norway, the United States, and China. The images have been annotated with more
than 55,000 instances of road damage. Four types of road damage, namely
longitudinal cracks, transverse cracks, alligator cracks, and potholes, are
captured in the dataset. The annotated dataset is envisioned for developing
deep learning-based methods to detect and classify road damage automatically.
The dataset has been released as a part of the Crowd sensing-based Road Damage
Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers
from across the globe to propose solutions for automatic road damage detection
in multiple countries. The municipalities and road agencies may utilize the
RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic
monitoring of road conditions. Further, computer vision and machine learning
researchers may use the dataset to benchmark the performance of different
algorithms for other image-based applications of the same type (classification,
object detection, etc.).Comment: 16 pages, 20 figures, IEEE BigData Cup - Crowdsensing-based Road
damage detection challenge (CRDDC'2022
Smartphone-based vehicle telematics: a ten-year anniversary
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment
Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management
Research on damage detection of road surfaces has been an active area of
re-search, but most studies have focused so far on the detection of the
presence of damages. However, in real-world scenarios, road managers need to
clearly understand the type of damage and its extent in order to take effective
action in advance or to allocate the necessary resources. Moreover, currently
there are few uniform and openly available road damage datasets, leading to a
lack of a common benchmark for road damage detection. Such dataset could be
used in a great variety of applications; herein, it is intended to serve as the
acquisition component of a physical asset management tool which can aid
governments agencies for planning purposes, or by infrastructure mainte-nance
companies. In this paper, we make two contributions to address these issues.
First, we present a large-scale road damage dataset, which includes a more
balanced and representative set of damages. This dataset is composed of 18,034
road damage images captured with a smartphone, with 45,435 in-stances road
surface damages. Second, we trained different types of object detection
methods, both traditional (an LBP-cascaded classifier) and deep learning-based,
specifically, MobileNet and RetinaNet, which are amenable for embedded and
mobile and implementations with an acceptable perfor-mance for many
applications. We compare the accuracy and inference time of all these models
with others in the state of the art
Surface monitoring of road pavements using mobile crowdsensing technology
Pavement-surface characteristics should be considered during road maintenance for safe and comfortable driving. A detailed and up-to-date report of road-pavement network conditions is required to optimize a maintenance plan. However, manual road inspection methods, such as periodic visual surveys, are time-consuming and expensive. A common technology used to address this issue is SmartRoadSense, a collaborative system for the automatic detection of road-surface characteristics using Global Positioning System receivers and triaxial accelerometers contained in mobile devices. In this study, the results of the SmartRoadSense surveys conducted on Provincial Road 2 (SP2) in Salerno, Italy, were compared with the Distress Cadastre data for the same province and the pavement condition indices of different sections of the SP2. Although the effectiveness of the crowdsensing-based SmartRoadSense was found to vary with the distress type, the system was confirmed to be very efficient for monitoring the most critical road failures
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