Learning pavement surface condition ratings through visual cues using a deep learning classification approach.

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. The manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and timeconsuming. This paper first analyses the complexity of the current manual visual rating system. This paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. The dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural environments taken from a video camera mounted in front of a van. The PSCI ratings were applied by experts using a scale of 1-10 to indicate surface conditions. The classification models are evaluated for different input pre-processing variations, image size, learning techniques, and the number of classes. Using 10 PSCI classes, the best classifier achieved a precision of 57% and a recall of 58%. Adjacent combination of classes (e.g., ratings 1 and 2 combined into a single class) to form a 5-class problem produced a classifier with a precision of 70% and recall of 77%. Given the complexity of the problem, classification using CNN holds promise as a first step towards an automated ranking system

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 paper proposes an automatic patch detection system using object detection technique. To our knowledge, this is the first time state-of-the-art object detection models Faster RCNN, and SSD MobileNet-V2 have been used to detect patches inside images acquired by LCMS. 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. The contribution of this paper are (1) an automatic pavement patch detection models for LCMS images and (2) comparative analysis of RCNN, and SSD MobileNet-V2 models for automatic patch detection

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

Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating

An application of damage cost allocation for airport services in Ireland

Paper presented at 6th International Conference on Managing Pavements, 19-24 October 2004, Brisbane, Australia.This paper describes a procedure developed for the estimation of marginal damage costs for airfield pavements in order to establish off-peak airport charges at Irish airports. The Commission for Aviation Regulation has regulated Irish airports with more than one million passengers per annum since 2001. The Irish government in order to separate the ownership and regulatory functions that had both been vested with the Minister for Transport established the Commission. The three main international airports are owned and operated by the publicly owned Aer Rianta. The relationships between the airport authority and its main customers had become increasingly hostile and confrontational on issues including landing charges in the previous five years. PMS Pavement Management Services Ltd was engaged by the Commission to develop a methodology for off-peak marginal costs based on damage caused to airport facilities. The procedure developed uses the ICAO Aircraft Classification Number (ACN) to determine and allocate damage costs among different aircraft types for charges in off-peak periods. A total of 18 aircraft damage categories were determined for aircraft using Dublin Airport, based on a combination of ACN and Maximum Take-Off Weight (MTOW). The predicted maintenance and rehabilitation costs for the airport pavement infrastructure were allocated among the damage categories. An equivalent cost per tonne for 5 aircraft cost categories was subsequently developed to simplify the administration of the system by the airport authorities. The system has replaced the previous charging system based on MTOW only, and is in operation since 2001. The charging mechanism more closely reflects the actual damage induced by different aircraft, and is encouraging airline operators to consider alternative aircraft types and gear configurations that induce lower damage for similar MTOW. Some modifications have been incorporated into the charging scheme based on a 2 year review of the system in 2003. Ultimately it is intended to require aircraft operators to certify ACN values rather than MTOW on an ongoing basis at Dublin Airport.Not applicableThis item has alisting on DSpace already http://hdl.handle.net/10197/116 Duplicate removed - OR 13/05/1

An application of damage cost allocation for airport services in Ireland

Paper presented at 6th International Conference on Managing Pavements, 19-24 October 2004, Brisbane, Australia.This paper describes a procedure developed for the estimation of marginal damage costs for airfield pavements in order to establish off-peak airport charges at Irish airports. The Commission for Aviation Regulation has regulated Irish airports with more than one million passengers per annum since 2001. The Irish government in order to separate the ownership and regulatory functions that had both been vested with the Minister for Transport established the Commission. The three main international airports are owned and operated by the publicly owned Aer Rianta. The relationships between the airport authority and its main customers had become increasingly hostile and confrontational on issues including landing charges in the previous five years. PMS Pavement Management Services Ltd was engaged by the Commission to develop a methodology for off-peak marginal costs based on damage caused to airport facilities. The procedure developed uses the ICAO Aircraft Classification Number (ACN) to determine and allocate damage costs among different aircraft types for charges in off-peak periods. A total of 18 aircraft damage categories were determined for aircraft using Dublin Airport, based on a combination of ACN and Maximum Take-Off Weight (MTOW). The predicted maintenance and rehabilitation costs for the airport pavement infrastructure were allocated among the damage categories. An equivalent cost per tonne for 5 aircraft cost categories was subsequently developed to simplify the administration of the system by the airport authorities. The system has replaced the previous charging system based on MTOW only, and is in operation since 2001. The charging mechanism more closely reflects the actual damage induced by different aircraft, and is encouraging airline operators to consider alternative aircraft types and gear configurations that induce lower damage for similar MTOW. Some modifications have been incorporated into the charging scheme based on a 2 year review of the system in 2003. Ultimately it is intended to require aircraft operators to certify ACN values rather than MTOW on an ongoing basis at Dublin Airport.Not applicableThis item has alisting on DSpace already http://hdl.handle.net/10197/116 Duplicate removed - OR 13/05/1

An application of dynamic programming to pavement management systems

Dynamic programming is employed in conjunction with a Markov chain probability-based prediction model to obtain minimum cost maintenance strategies over a given life-cycle analysis period. The output is taken in conjunction with a specified budget and run through a prioritization package to produce a prioritized list of sections with their recommended maintenance alternatives and cost for each year in which a budget is specified. Pavement sections are split into families on the basis of characteristics such as surface types, distress modes and traffic levels. Prediction curves are fitted using Markov chain theory to obtain transition probability values that define future performance in terms of PCI states. Various maintenance alternatives are considered, and the initial cost of applying each alternative in each state for each family is input as are the probability values associated with the predicted performance of the maintenance alternatives in the future. Any life-cycle period can be specified, and any interest rate can be used. The dynamic programming algorithm then takes these inputs and, simultaneously for every state in every family, outputs the desired maintenance alternatives that will minimize the total expected cost over a specified life-cycle subject to keeping all sections above pre-defined performance standards. The algorithm is very simple, extremely fast and produces guaranteed global optimal solutions. A simulation routine is used to estimate the variance associated with the mean cost estimates given by dynamic programming. These programs were rigorously tested using four databases. Changes in output as the input parameters were changed were analyzed. The outputs from the dynamic programming probabilistic approach were compared with results obtained from a deterministic strategy analysis, and were very similar. A prioritization program was developed to apply budget constraints and derive the best set of sections to be repaired within these constraints for each year specified. This program uses the outputs from dynamic programming and weighting factors related to specific section characteristics in conjunction with a heuristic approach to obtain the desired list of prioritized sections

Airport Services and Airport Charging Systems: A Critical Review of The EU Common Framework

This paper reviews proposals contained in the Commission of the European Communities (CEC) Consultation Paper of 1995 and Draft Directive of 1997 on Airport Charges. Economic and practical problems associated with the CEC proposals are highlighted. The main conclusions are that the CEC proposals will be difficult to enforce because they are vague, implementation is left to the individual member states, and because they allow for a variety of approaches to charging systems which can include substantial cross-subsidisation across aeronautical and non-aeronautical uses at one or more airports. The fact that the Commission will not have significant powers of enforcement further reduces the impact which the proposals can have.airport charging systems marginal cost pricing CEC Airport Charges Directive