Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.https://arrow.tudublin.ie/cddpos/1016/thumbnail.jp

Methodology of Transportation System Management

In this paper, we propose an improved design methodology to meet the changing demands of an existing transport system available in rural or urban areas. This is necessary to focuses on the convenience and comfort for road users. Effective control of the overall transport system is essential to enhance the safety and convenience. However, onion changes in population coupled with the economic development, mean that most of the control and management methods can quickly become dated or even have been unsuitable, leading to the vehicle development. This paper thus seeks to develop a practical and efficient methodology to allow current transport system to be updated to alleviate congestion. For this purpose we undertake a comparison between internal transport system and urban traffic. The methodology useful for many applications which are necessary for development in current transport system

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the I-ROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as "Good"/"Preventive Maintenance", "Fair"/"Rehabilitation", "Poor"/"Reconstruction", which are ranges of the custom PCI ranting scale and represents a typical repair strategy

Deep learning-based automated detection and clustering of potholes using variational autoencoder for efficient road maintenance

In this study we explore the possibility of using unsupervised learning for pothole detection. We will use images of roads from Brazil where both potholes and cracks can be present in the images, as well as clean images with no damage on the road. This will be done using a variational autoencoder (VAE) and clustering. The study will also explore a supervised method, support vector machine (SVM), to compare the performance of supervised model vs. unsupervised model. The goal for this study is to correctly cluster images containing potholes from images that do not contain potholes

Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models

Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models

Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Texton Based Segmentation for Road Defect Detection from Aerial Imagery

Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score

A Framework for Automated Pavement Condition Monitoring

Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task. In this paper a framework that automates the process is presented. Video data collected from the car’s parking camera is utilized to detect defects in frames. Simultaneously, elevation signals collected from accelerometers attached to the car are processed to reconstruct the profile of the road and detect defects associated with its z-axis, such as bumps. A GPS device is synchronized with the other sensors to acquire the data’s geolocation. Detected defects are then classified according to their type and their severity is assessed. All information is then transferred via 4G network to a central server, where the Road Condition Index of road segments necessary to classify roads is calculated. Finally, everything is saved in a Pavement Management System. Preliminary results on the processing of video data demonstrate the frameworks’ promising application. The initial identification of frames including defects produces an accuracy of 96% and approximately 97% precision. Further experiments on such frames, aiming at the detection of potholes, patches and three different types of cracks result in over 84% overall accuracy and over 85% precision.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.07