Query: pothole detection

title: Pothole Detection Based on Disparity Transformation and Road Surface Modeling
abstract: Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu's thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel level accuracy is approximately 99.6%

title: Pothole Detection Using Computer Vision and Learning
abstract: Techniques for identifying potholes on road surfaces aim at developing strategies for real time or offline identification of potholes, to support real time control of a vehicle (for driver assistance or autonomous driving) or offline data collection for road maintenance. For these reasons, research around the world has comprehensively explored strategies for the identification of potholes on roads. This paper starts with a brief review of the field; it classifies developed strategies into several categories. We, then, present our contributions to this field by implementing strategies for automatic identification of potholes. We developed and studied two techniques based on stereo vision analysis of road environments ahead of the vehicle; we also designed two models for deep learning based pothole detection. An experimental evaluation of those four designed methods is provided, and conclusions are drawn about particular benefits of these methods.

title: Pothole detection using location aware convolutional neural networks
abstract: Poor road conditions, such as potholes, are a nuisance to society, which would annoy passengers, damage vehicles, and even cause accidents. Thus, detecting potholes is an important step toward pavement maintenance and rehabilitation to improve road conditions. Potholes have different shapes, scales, shadows, and illumination effects, and highly complicated backgrounds can be involved. Therefore, detection of potholes in road images is still a challenging task. In this study, we focus on pothole detection in 2D vision and present a new method to detect potholes based on location aware convolutional neural networks, which focuses on the discriminative regions in the road instead of the global context. It consists of two main subnetworks: the first localization subnetwork employs a high recall network model to find as many candidate regions as possible, and the second part based subnetwork performs classification on the candidates on which the network is expected to focus. The experiments using the public pothole dataset show that the proposed method could achieve high precision (95.2% recall (92.0% simultaneously, and outperform the most existing methods. The results also demonstrate that accurate part localization considerably increases classification performance while maintains high computational efficiency. The source code is available at https:/github.com/hanshenchen/pothole detection

title: We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
abstract: Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the individual experience. Our recently introduced disparity (or inverse depth) transformation algorithm allows better discrimination between damaged and undamaged road areas, and it can be easily deployed to any semantic segmentation network for better road pothole detection results. To boost the performance, we propose a novel attention aggregation (AA) framework, which takes the advantages of different types of attention modules. In addition, we develop an effective training set augmentation technique based on adversarial domain adaptation, where the synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks. The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA UNet and AA RTFNet, our best performing implementations, respectively outperform all other state of the art single modal and data fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state of the art semantic segmentation networks, but also accelerates their convergence. Source Code and Dataset: http:/sites.google.com/view/pothole 600 R. Fan and H. Wang These authors contributed equally to this work and are therefore joint first authors. c (c) Springer Nature Switzerland AG 2020 A. Bartoli and A. Fusiello (Eds. ECCV 2020 Workshops, LNCS 12538, pp. 1 16, 2020. https:/doi.org/10.1007/978 3 030 66823 5_17

title: DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT

title: Pothole Detection System
abstract: Here we propose design of 'Pothole detection System' which assists the driver in avoiding pot holes on the roads, by giving him prior warnings. Warnings can be like buzzer if the driver is approaching a pothole, or driver may be warned in advanced regarding what road has how many potholes. This system is divided into three subsystems. First is sensing subsystem which senses the potholes encountered by it, about which it did not have the prior information. Then communication subsystem which transfers the information between Wi Fi access point and mobile node. Access Point broadcasts the data about potholes in its area. When vehicle gets this data vehicle sees if it has sensed any potholes which access point does not have information about; if it is the case then the information about those potholes is transmitted to access point as a feedback. Access Point updates its database with the new entries of potholes. And finally the localization subsystem which reads the data given by Access points and warns the driver regarding the occurrence of pothole.

title: A Deep Learning Approach for Street Pothole Detection
abstract: Potholes are a structural damage to the road with hollow which can cause severe traffic accidents and impact road efficiency. In this paper, we propose an efficient pothole detection system using deep learning algorithms which can detect potholes on the road automatically. Four models are trained and tested with preprocessed dataset, including YOLO V3, SSD, HOG with SVM and Faster R CNN. In the phase one, initial images with potholes and non potholes are collected and labeled. In the phase two, the four models are trained and tested for the accuracy and loss comparison with the processed image dataset. Finally, the accuracy and performance of all four models are analyzed. The experimental results show that the YOLO V3 model performs best for its faster and more reliable detection results.

title: A Modern Pothole Detection technique using Deep Learning
abstract: Road accident detection and avoidance are a more difficult and challenging problem in India as poor quality of construction materials get used in road drainage system construction. Due to the above problems, roads get damaged early and potholes appear on the roads which cause accidents. According to a report submitted by the Ministry of Road Transport and Highways transport research wing New Delhi in 2017, approximately 4,64,910 accidents happen per year in India. This paper proposed a deep learning based model that can detect potholes early using images and videos which can reduce the chances of an accident. This model is basically based on Transfer Learning, Faster Region based Convolutional Neural Network(F RCNN) and Inception V2. There are many models for pothole detection that uses the accelerometer (without using images and videos) with machine learning techniques, but a less number of pothole detection models can be found which uses only machine learning techniques to detect potholes. The results of this work have shown that our proposed model outperforms other existing techniques of potholes detection.

title: Review on Analysis Techniques for Road Pothole Detection
abstract: Due to various road defects such as potholes, cracks, patches, rutting, and shoving, condition of the road becomes poor which is a cause to increases accidents on road. Therefore, road condition assessment is extremely necessary when developing road maintenance systems. Currently, data collection process is done automatically by using different imaging techniques, but for defects assessment, analysis of the collected data is performed manually, which slow down the process. Existing approaches for pothole detection are vibration based, 3D reconstruction based, and vision based methods. This paper aims to present review on road defect detection techniques.

title: Efficient pothole detection using smartphone sensors
abstract: Road safety remains a casualty in India, with potholes wrecking asphalt pavements by the dozens. A study in 2017 recorded that potholes caused the budget for road safety to increase by a whopping 100.4 per cent, and even doubled the death toll from that of the year prior. To address this situation, an effective solution is required that ensures the drivers' safety and can prove beneficial for long term measures. This can be established by employing an apt pothole detection system which is simple yet functional. In this paper, the method for such a system is described which uses accelerometer and gyroscope, both built in the modern day smartphones, to sense potholes. Pothole induced vibrations can be measured on the axis reading, making them distinguishable. Our proposed Neural Network model is trained and evaluated on the data acquired from the sensors and classifies the potholes from the non potholes. The neural network gives a classification accuracy of 94.78 per cent. It also presents a solid precision recall trade off with 0.71 precision and 0.81 recall, considerably high for a problem with class imbalance. The results indicate that the method is suitable for creating an accurate and sensitive supervised model for pothole detection.