Coverage and mobile sensor placement for vehicles on predetermined routes: a greedy heuristic approach

Road potholes are not only nuisance but can also damage vehicles and pose serious safety risks for drivers. Recently, a number of approaches have been developed for automatic pothole detection using equipment such as accelerometers, image sensors or LIDARs. Mounted on vehicles, such as taxis or buses, the sensors can automatically detect potholes as the vehicles carry out their normal operation. While prior work focused on improving the performance of a standalone device, it simply assumed that the sensors would be installed on the entire fleet of vehicles. When the number of sensors is limited it is important to select an optimal set of vehicles to make sure that they do not cover similar routes in order to maximize the total coverage of roads inspected by sensors. The paper investigates this problem for vehicles that follow pre-determined routes, formulates it as a linear optimization problem and proposes a solution based on a greedy heuristic. The proposed approach has been tested on an official London bus route dataset containing 713 routes and showed up to 78% improvement compared to a random sensor placement selected as a baseline algorithm

CPD: Crowd-based Pothole Detection

Potholes and other damages of the road surface constitute a problem being as old as roads are. Still, potholes are widespread and affect the driving comfort of passengers as well as road safety. If one knew about the exact locations of potholes, it would be possible to repair them selectively or at least to warn drivers about them up to their repair. However, both scenarios require their detection and localization. For this purpose, we propose a crowd-based approach that enables as many of the vehicles already driving on our roads as possible to detect potholes and report them to a centralized back-end application. Whereas each single vehicle provides only limited and imprecise information, it is possible to determine these information more precisely when collecting them at a large scale. These more exact information may, for example, be used to warn following vehicles about potholes lying ahead to increase overall safety and comfort. In this work, this idea is examined and an offline executable version of the desired system is implemented. Additionally, the approach is evaluated with a large database of real-world sensor readings from a testing fleet and therefore its feasibility is proved. Our investigation shows that the suggested CPD approach is promising to bring customers a benefit by an improved driving comfort and higher road safety

An Optimized Deep Learning Framework For Pothole Detection

Pothole detection plays a crucial role in preventing road accidents and is effective in establishing road maintenance and safety. Although various pothole detection models are designed to accurately identify the pothole based on road images, they face issues in accuracy and hyperparameter tuning. The presented research work concentrates on developing a novel optimized deep learning model for the accurate prediction of potholes on the road infrastructure using the recurrent neural network (RNN) and grey wolf optimization (GWO). Initially, the road images are collected and pre-processed. The pre-processing includes the removal of noises, image resizing, etc., to improve the image quality. Further, texture-based feature extraction was employed to extract the most relevant features from the pre-processed image. Then, the RNN architecture was trained using the extracted features to learn the interconnections between the image features and pothole detection. In addition, the GWO fitness solution was integrated into the classification module to tune and optimize the RNN hyperparameters, which increases the detection performances such as accuracy, and reduces the loss function. Finally, the presented model was evaluated with the publically available road image detection database and the outcomes are determined. The performance assessment demonstrates that the designed model attained greater accuracy of 98.76%, and a loss function of 0.06. Furthermore, a comparative assessment was performed with existing methods to evaluate the effectiveness of the proposed model

Detection of Pothole by Applying Convolutional Neural Network and Random Forest Techniques

Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formation of potholes and cracks on the roads, which can cause damage to vehicles or pose a physical danger to occupants, particularly in underdeveloped countries. Identifying potholes in real-time can help drivers avoid them and prevent accidents. Furthermore, recording their locations and sharing them can assist other drivers and road maintenance organizations take prompt corrective measures. In our attempt to address the issue of pothole detection, we aim to combine the latest technological advancements. We aim to develop practical, reliable, adaptable, and modular solutions. To achieve this, we will compare the performance of Random Forest, a machine learning model, with CNN, a deep learning model, in detecting potholes. We will train these models using multiple datasets and analyse their performance to determine their effectiveness in pothole detection

Pothole Detection under Diverse Conditions using Object Detection Models

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalisable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles, and resolutions. In this paper, we present our approach to building a generalized learning model for pothole detection. We apply four datasets that contain a range of image and environment conditions. Using the Faster RCNN object detection model, we demonstrate the extent to which pothole detection models can generalise across various conditions. Our work is a contribution to bringing automated road maintenance techniques from the research lab into the real-worl

Building a pothole detection and tracking system

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, April 2019Building and maintaining infrastructure is often a key challenge in developing countries, and Ghana is no exception. Increasing population and car ownership rates coupled with poor maintenance cultures result in a corresponding increase in the rate of damage of roads, causing deformities such as cracks and potholes. These road deformities not only negatively impact a country’s road infrastructure and the cars which ply said roads, but also pose a threat to road users. In Ghana, only two mobile maintenance units are charged with monitoring the roads in all ten regions of the country. Thus, this project presents Pothole Tracker Ghana, a two-tiered application inspired by the idea of crowdsourcing. Consisting of a vision-based pothole classification system implemented on a Raspberry Pi and a map-based web application, this project aims to reduce the barriers to data collection on poor road infrastructure on the part of governments whilst allowing everyday road users to make informed decisions concerning their journeys. Three different algorithms are considered and compared for the classification task; logistic regression, support vector machines (SVM) and a hybrid algorithm incorporating a convolutional neural network (CNN) and SVM. The tuned SVM is chosen for the final system implementation. 
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