Automated Detection of Multiple Pavement Defects

Knowing the pavement condition is essential for efficiently deciding on maintenance programs. Current practice is predominantly manual with only 0.4% of inspections happening automatically. All methods in the literature aiming at automating condition assessment focus on two defects at most, or are too expensive for practical application. In this paper, the authors propose a low-cost method that automatically detects pavement defects simultaneously using parking camera video data. The types of defects addressed in this paper are two types of cracks (longitudinal and transverse), patches, and potholes. The method uses the semantic texton forests (STFs) algorithm as a supervised classifier on a calibrated region of interest (myROI), which is the area of the video frame depicting only the usable part of the pavement lane. It is validated using data collected from the local streets of Cambridge, U.K. Based on the results of multiple experiments, the overall accuracy of the method is above 82%, with a precision of more than 91% for longitudinal cracks, more than 81% for transverse cracks, more than 88% for patches, and more than 76% for potholes. The duration for training and classifying spans from 25 to 150 min, depending on the number of video frames used for each experiment. The contribution of this paper is dual: (1) an automated method for detecting several pavement defects at the same time, and (2) a method for calculating the region of interest within a video frame considering pavement manual guidelines.This material is based in part upon work supported by the National Science Foundation under Grant Number 1031329.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via https://doi.org/10.1061/(ASCE)CP.1943-5487.000062

GIS-Based Road Transport Infrastructure Management System for Adamawa Central, Adamawa State, Nigeria

This study focus on the GIS-Based Road Transport infrastructure Management System for Adamawa Central, Adamawa State, Nigeria. The study covered Adamawa central which comprises of seven local government areas namely; Yola North (Jimeta), Yola South, Fufore, Gerei, Song, Gombi and Hong. Satellite images, road transport map, road transport documents, bridges pictures, road pictures as well as the bridges and the roundabout coordinate were all used to obtain the final results for the study. The satellite images were Spot image of 2012 and Geogle Earth image of 2013. The satellite images and road map were used in updating the road transport map, the road transport documents as well the road, bridge and roundabout picture were used as an inventory in building the geodatabase for the GIS-Based road transport infrastructure management system and some of the roads, bridge and roundabout coordinates were used for hyperlinking the pictures to the spatial reference.ArcGIS 10.1, Microsoft 2013 and AO scanner was used for the entire thesis work, the thesis critically observed the process involved in GIS-Based road transport infrastructure Management system for the selected road transport Infrastructures for Adamawa central, analysis were performed for proper decision-making on how to manage the  road transport infrastructures. The result reveals that Geographic Information System as a very important system can be used in data collection, entry, development, management and analysis. The research also show that the process of converting the traditional database system to a Geographical Information System (GIS) required in the planning will and commitment. It is recommended that government should establish GIS unit in the federal and state ministry of transports board and also encourage the local government areas to do the same for proper planning and development of road transport infrastructure and management system for easy management and control of its facilities. Keywords: ArcGIS 10.1, GIS, bridges pictures, road pictures, Road Transport Inventory, Road Transport Map, Road Transport Documents, Geodatabase. Sport Image, Google Earth Imag

Road Maintenance through Machine Learning

This thesis explores the use of machine learning techniques for road infrastructure maintenance. We propose an innovative machine learning-based approach to improve the efficiency and effectiveness of road maintenance strategies. The focal point of this investigation is the development and implementation of a machine learning framework to enhance road quality monitoring. We use Long Short-Term Memory (LSTM) networks to accurately predict future road conditions and identify potential areas requiring maintenance before significant deterioration occurs. This predictive approach is designed to enable a shift from reactive to proactive road maintenance, optimizing the use of limited resources and improving overall road safety. The methodology of the research is structured in three phases: the creation of a prototype system for road condition data collection, the application of LSTM networks for predictive analysis, and the utilization of optimization techniques to guide effective maintenance decisions. By focusing on predictive accuracy and the strategic allocation of maintenance efforts, the study seeks to extend the lifespan of road infrastructure, reduce maintenance costs, and enhance the driving experience. This thesis is a contribution to the field of road infrastructure maintenance by introducing a predictive maintenance model that leverages advanced machine learning techniques. It aims to transform the traditional maintenance approach, providing a scalable and efficient solution to road infrastructure management challenges, with the potential to significantly influence policy and practice in infrastructure maintenance.KEYWORDS: Machine learning; Infrastructure maintenance; Proactive maintenanc

Improving root cause analysis through the integration of PLM systems with cross supply chain maintenance data

The purpose of this paper is to demonstrate a system architecture for integrating Product Lifecycle Management (PLM) systems with cross supply chain maintenance information to support root-cause analysis. By integrating product-data from PLM systems with warranty claims, vehicle diagnostics and technical publications, engineers were able to improve the root-cause analysis and close the information gaps. Data collection was achieved via in-depth semi-structured interviews and workshops with experts from the automotive sector. Unified Modelling Language (UML) diagrams were used to design the system architecture proposed. A user scenario is also presented to demonstrate the functionality of the system

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

Development of GIS-Based Road Transport Information Management System for Adamawa Central, Adamawa State, Nigeria

This study was conducted on the Development of Gis-Baesd Road Transport Information Mamagenmet System for Adamawa Central, Adamawa State, Nigeria. The study covered Adamawa central which comprises of seven local government areas nanell; Yola North (Jimeta), Yola South, Fufore, Gerei, Song, Gombi and Hong. Satellite images, road transport map, road transport documents, as well as the bridges and the roundabout coordinate were all used to obtain the final results for the study. The satellite images were Spot image of 2012 and Geogle Earth image of 2013. The satellite images and road map were used in updating the road transport map, the road transport documents as well the road, bridge and roundabout picture were used as an inventory in building the geodatabase for the development of the GIS-Based road transport iformation management system and some of the roads, bridge and roundabout coordinates were used for hyperlinking the pictures to the spatial reference.ArcGIS 10.1, Microsoft 2013 and AO scanner was used for the entire thesis work, the thesis critically observed the process involved in Developing a GIS-Based road transport information Management system for the various road transport Infrastructures for Adamawa central, analysis were performed for proper decision-making on how to manage the  road transport infrastructures. The result reveals that Geographic Information System as a very important system can be used in data collection, entry, development, management and analysis. The research also show that the process of converting the traditional database system to a Geographical Information System (GIS) does not require the hi-tech knowledge and equipment common in science fictions and movies, but what is required in the planning will and commitment. It is recommended that government should establish GIS unit in the federal and state ministry of transports board and also encourage the local government areas to do the same for proper planning and development of road transport infrastructure and management for easy management and control of its facilities. Keywords: ArcGIS 10.1, GIS, Road Transport, Road Transport Inventory, Road Transport Map, Road Transport Documents, Geodatabase. Sport Image, Google Earth Imag

Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction

OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving

Surround View fisheye cameras are commonly deployed in automated driving for 360\deg{} near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results.Comment: Camera ready version accepted for RA-L and ICRA 2021 publicatio