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Detection of Potholes and Speed Breaker on Road
... Some of the recent studies have proposed ultrasonic sensors based approaches to identify potholes cost effectively [3,11,14]. According to the researchers in [3], such system can be implemented by installing the sensor outside on the car, facing downwards towards the road. ...
... This data can then be transferred to an android application, web application or a central database for further use. [11] proposed a pothole detection system which they tested on a toy car with artificial potholes and speed breakers. They set the threshold value for the depth of pothole to be from 4.98cm to 5.02cm and successfully tested the system. ...
Developing countries do not have satisfactory road conditions as they have cracks, humps and other irregularities. Such roads are one of the main causes of road accidents. Therefore, condition of the road surfaces need to be assessed timely to minimize these damages. Detecting cracks and potholes and reporting them is performed manually most of the times. However, with the advent of internet of things, researches are performed to automate this process of finding road surface conditions. In this paper, a comparative analysis is carried out among the present automated road cracks sensing solutions on the basis of different factors. Moreover, a real time and cost efficient sensing solution is also proposed in this research paper.
... There has been a substantial amount of research done in the past on the detection of potholes [3][4][5] using various techniques like reconstruction using laser [6], stereo vision [7,8], Light Detection and Raanging (LIDAR) [9], ultrasonic [10][11][12][13], etc. Forrest et al. [10] proposed a method of detecting road surface disruptions based on ultrasonic sensors in which the pothole detection rate of 62% was achieved on a paved road. Singh et al. [11] used a global positioning system receiver and ultrasonic for the identification of geographical location coordinates of the detected potholes and speed breaker with 59.23% efficiency. ...
... There has been a substantial amount of research done in the past on the detection of potholes [3][4][5] using various techniques like reconstruction using laser [6], stereo vision [7,8], Light Detection and Raanging (LIDAR) [9], ultrasonic [10][11][12][13], etc. Forrest et al. [10] proposed a method of detecting road surface disruptions based on ultrasonic sensors in which the pothole detection rate of 62% was achieved on a paved road. Singh et al. [11] used a global positioning system receiver and ultrasonic for the identification of geographical location coordinates of the detected potholes and speed breaker with 59.23% efficiency. Madli et al. [12] worked on the automatic detection and notification of potholes and humps on roads to aid drivers using ultrasonic and a Global Positioning System (GPS). ...
Featured Application: Road surface monitoring for severe conditions. Abstract: Road surface monitoring is an essential problem in providing smooth road infrastructure to commuters. This paper proposed an efficient road surface monitoring using an ultrasonic sensor and image processing technique. A novel cost-effective system, which includes ultrasonic sensors sensing with GPS for the detection of the road surface conditions, was designed and proposed. Dynamic time warping (DTW) technique was incorporated with ultrasonic sensors to improve the classification and accuracy of road surface detecting conditions. A new algorithm, HANUMAN, was proposed for automatic recognition and calculation of pothole and speed bumps. Manual inspection was performed and comparison was undertaken to validate the results. The proposed system showed better efficiency than the previous systems with a 95.50% detection rate for various road surface irregularities. The novel framework will not only identify the road irregularities, but also help in decreasing the number of accidents by alerting drivers.
... In [25], the location of detected potholes was marked on Google Maps and shared with drivers via a mobile app. Additionally, [40] estimated the height and depth of road abnormalities in addition to detecting potholes. Another notable work is TRACTS-Net presented in [60], which introduced an intelligent road damage detection system that leverages USens specifically for pothole detection. ...
RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SP-based monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.
... The authors in [10] propose a pothole detection system using accelerometer data. When the accelerometer data exceeds the configured detection threshold value, the information is sent to a cloud database. ...
... Another solution to address the problem of roadside service provision is proposed in (Singh et al., 2018). This work provides a discussion on pothole detection systems. ...
Vehicular Ad hoc Network (VANET) is the cutting edge technology for smart transportation. VANET becomes an important aspect of the Intelligent Transport System (ITS). Different safety and non-safety applications have been developed for VANET. The inspiration behind VANET is to provide safe, and pleasant journeys to the drivers and passengers. Although the quality of software depends upon its architecture, most of them do not give proper attention to the consideration of Software-Oriented Architecture (SOA) for providing safety and non-safety ITS services in VANET. To address this issue, we proposed an efficient software architecture by highlighting the important operations and services of the system. The performance of the proposed architecture is evaluated by several design metrics and the results are compared with a state-of-the-art solution. The results showed that our proposed architecture has low coupling and high cohesion factors. Furthermore, the results reveal that our architecture is less complex and more reusable. From the results, we conclude that the proposed architecture is suitable for providing safety and non-safety ITS services and will pave the way for the implementation of the futuristic vision of the ITS.
... As per government norms, a speed breaker should be of 10cm in height and 3.7m in width and must be painted with bands of white and black luminary paint for greater visibility and smooth travel experience. Moreover, it is compulsory to put a cautionary sign at 40m distance before the speed breaker [2] - [5]. However, it is observed that most speed breakers in India are 1 − 2m wide with height greater than 10cm and little or no paint on them. ...
... Pothole is the most common form of distress on cement concrete pavement [2,3] with a minimum dimension of 150 mm [4]. Potholes have significant influences on the running quality of vehicles [5,6], and they can compromise pavement ridability and safety and can even be the cause of major accidents [6][7][8][9]. erefore, the focus of road maintenance is to handle potholes [10]. Rapidly and accurately detecting potholes in cement pavement is an important prerequisite for the road management department in formulating scientific and effective maintenance strategies and implementing distress treatment [6,11,12]. ...
Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.
The advancement of autonomous vehicles necessitates robust systems for real-time detection of lane markings and speed breakers to ensure safe and efficient navigation. In this paper, we propose a novel approach for lane and speed breaker detection utilizing Convolutional Neural Networks (CNN). Our method achieves remarkable accuracy in identifying lane markings and speed breakers from input images captured by onboard cameras. By leveraging the capabilities of CNN, our system demonstrates superior performance in diverse environmental conditions, including varying lighting, weather, and road surface conditions. We present a comprehensive analysis of the CNN architecture employed in our system, highlighting its ability to extract meaningful features from raw image data and make accurate predictions. The training process involves a large dataset comprising annotated images of lane markings and speed breakers, enabling the CNN to learn discriminative patterns for effective detection. Furthermore, we evaluate the performance of our proposed system through extensive experimentation, quantifying its accuracy, precision, and recall rates across different scenarios. The results indicate the effectiveness and reliability of our approach in real-world settings, showcasing its potential for integration into autonomous vehicle platforms. Overall, this paper contributes to the field of autonomous driving by presenting a robust and efficient solution for lane and speed breaker detection, leveraging the power of machine learning algorithms, specifically CNN, to enhance the safety and autonomy of vehicles on the road.
Advanced speed breaker is a system that helps to promote road education, contributes to respecting speed limits, benefiting road and drive safety, with the aim of preventing accidents and raising awareness among drivers of respecting speed limits, also compiling statistics and making possible measures impact and benefits. Advanced Speed Breaker could be a business safety system where dashing vehicles spark the speed swell and rises the speed bumps on top of the paved surface and giving the physical remainder to motorist to decelerate down the vehicle. If the speed of the on-going vehicles is inside the predetermine limit then the speed bumps keep flat on paved surface and vehicles passes over it well. This configuration detects the vehicles that circulate respecting the speed limit allowed in the area and lowers the device to ground level for them, but leaving it elevated for those who do not respect predetermine limit. Additional modifications can also be made to make emergency vehicles accessible.
Rainfall prediction is the highest research priority in flood-prone areas across the world. This work assesses the abilities of the Decision Tree (DT), Distributed Decision Tree (DDT), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbour (KNN), and Fuzzy Logic Decision Tree (FDTs) machine learning algorithms for the rainfall prediction across the Kashmir province of the Union Territory of Jammu & Kashmir. On application of Machine learning algorithms on geographical datasets gave performance accuracy varying from (78.61–81.53)%. Further again machine learning algorithms were reapplied on the dataset without season variable yet again performance ranged in between (77.5–81)%. Vigorous analysis has established that these machine learning models are robust and our study has established that the dataset reaches performance stagnation and thus resulting in performance capping. The stagnation is irrespective of the choice of algorithm and the performance shall not improvise beyond a specific value irrespective of the choice of the machine learning algorithm.
Pavement distress and wear detection is of prime importance in transportation engineering. Due to degradation, potholes and different types of cracks are formed and they have to be detected and repaired in due course. Estimating the amount of filler material that is needed to fill a pothole is of great interest to prevent any shortage or excess, thereby wastage, of filler material that usually has to be transported from a different location. Metrological and visualization properties of a pothole play an important role in this regard. Using a low-cost Kinect sensor, the pavement depth images are collected from concrete and asphalt roads. Meshes are generated for better visualization of potholes. Area of pothole is analyzed with respect to depth. The approximate volume of pothole is calculated using trapezoidal rule on area-depth curves through pavement image analysis. In addition pothole area, length, and width are estimated. The paper also proposes a methodology to characterize potholes.
A study by US Federal Highway Administration has shown that road condition is an essential factor of highway quality and smooth roads will lead to more comfortable driving experience and less municipal investment. International Roughness Index (IRI) has been widely used to measure pavement smoothness because it can provide a consistent rating for different measurement tools. However, existing measuring tools based on IRI are usually very expensive. In this paper, we present a low-cost vehicle-based solution, Road Condition Monitoring with Three-axis Accelerometers and GPS Sensors (RCM-TAGPS), by using a cheap three-axis accelerometer and a GPS sensor embedded in a vehicle to monitor the road condition. We analyze the Power Spectral Density (PSD) of pavement roughness, estimate IRI, and classify the pavement roughness level into four levels according to a Chinese industry standard. Experimental results show that RCM-TAGPS can evaluate pavement roughness level correctly, even under some interference like potholes, manholes and decelerating belts, and the total cost of RCM-TAGPS in each vehicle is no more than 50 dollars, which is about 1/4400 to 1/160 of the existing system used in civil engineering and municipal engineering.
This paper describes an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle. The sensor is based on the measurement of the time of flight of an ultrasonic pulse, which is reflected by the ground. A constrained optimization technique is employed to obtain reflected pulses that are easily detectable by means of a threshold comparator. Such a technique, which takes the frequency response of the ultrasonic transducers into account, allows a sub-wavelength detection to be obtained. Experimental tests, performed with a 40 kHz piezoelectric-transducer based sensor, showed a standard uncertainty of 1 mm at rest or at low speeds; the sensor still works at speeds of up to 30 m/s, although at higher uncertainty. The sensor is composed of only low cost components, thus being apt for first car equipment in many cases, and is able to self-adapt to different conditions in order to give the best results.
In this paper, we propose a centralized system which detects potholes on roads and assists the driver to avoid them. The system consists of 3 components namely sensors for detection, an inter-vehicle communication protocol to warn other vehicles about the new potholes which are detected, a central server which maintains a database of all the potholes. The system also consists of a GPS navigation system which will route the vehicle through an efficient path based on certain paramaters such as length of the route, intensity of potholes in the route, vehicle load and type, size limits of the road etc. In addition to this, the central server is configured to assign priority to the potholes and report high priority potholes to any local council such as a municipality so that they are repaired immediately.
Pothole avoidance may be considered similar to other obstacle avoidance, except that the potholes are depressions rather than extrusions from a surface. This study discusses a solution for detection of potholes in the path of an autonomous vehicle operating in an unstructured environment. Here, a vision approach is used since the simulated potholes are significantly different from the background surface. Furthermore, using this approach, pothole can only be detected in case of uniform lighting conditions. The solution to the problem is developed in a systematic manner. Initially, a specific camera and frame grabber are chosen, then camera is mounted on top of the autonomous vehicle and the images will be acquired. Then, a software solution is designed using MATLAB. The method is tested under real-time conditions and results demonstrate its reasonable efficiency.
The sensing, computing and communicating capabilities of smart phones bring new possibilities for creating smart applications, including in-car mobile applications for smart cities. However, due to the dynamic nature of vehicles, many requirements such as sensor management, signal and image processing or information sharing needs exist when developing a smart sensor-based in-car mobile application. On the other hand, most in-car applications generally employ single-modal sensor analysis, which also yields limited results. Using the advanced capabilities of smart phones, this study proposes a framework with built-in multimodal sensor analysis capability, and enables easy and rapid development of signal and image processing-based smart mobile applications. Within this framework, an abstraction for fast access to synchronized sensor readings, a plug in based multimodal analysis interface for signal and image processing applications, and a toolset to connect to other users or servers for sharing the results are provided built-in. As part of this study, a sample mobile application is also developed to demonstrate the applicability of the framework. This application is used for detecting defects on the road, such as potholes and speed bumps, and it automatically extracts the video section and the image of the corresponding road segment containing the defect. Upon such critical hazard detection, the application instantly informs nearby users about the incident. A good detection rate of speed bumps is obtained in the performed tests, while the advantage of automatic image extraction based on the multimodal approach is also demonstrated.
Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. The cameras used by most of the AIAS are based on Charge-Coupled Device (CCD) image sensors where a visible ray is projected. However, the quality of the images captured by the CCD cameras was limited by the inconsistent illumination and shadows caused by sunlight. To enhance the CCD image quality, a high-power artificial lighting system has been used, which requires a complicated lighting system and a significant power source. In this paper, we will introduce an efficient and more economical approach for pavement distress inspection by using laser imaging. After the pavement images are captured, regions corresponding to potholes are represented by a matrix of square tiles and the estimated shape of the pothole is determined. The vertical, horizontal distress measures, the total number of distress tiles and the depth index information are calculated providing input to a three-layer feed-forward neural network for pothole severity and crack type classification. The proposed analysis algorithm is capable of enhancing the pavement image, extracting the pothole from background and analyzing its severity. To validate the system, actual pavement pictures were taken from pavements both in highway and local roads. The experimental results demonstrated that the proposed model works well for pothole and crack detection.
This paper presents a road surface defect identification system based on 3D accelerometers, GPS and video modules deployed on vehicles. The mobile platform architecture and the central data aggregation algorithm are also discussed. Because the mobile system is deployed over a large outdoor area, we also present a solution for the wireless communication coverage problem. Finally, we are highlighting the importance of the gathered information by making it available for the users using a GIS platform.
In order to detect the three-dimensional cross-section of pavement pothole more effectively, this paper proposes a method which employs optical imaging principle of three-dimensional projection transformation to obtain pictorial information of pothole's cross-section in pothole detection. Multiple digital image processing technologies, including: image preprocessing, binarization, thinning, three-dimensional reconstruction, error analysis and compensation are conducted in the series of image analysis and processing. Experimental results indicate that the method is markedly superior to traditional methods in many aspects. For its simple detection principle, low cost and high efficiency, the method suggests great practical and promoting value.
There are much more researches on the recognition of the cracks on the distress pavement, but the research on the potholes is relatively less. In this paper, Texture measure based on the histogram is extracted as the features of the image region, and the non-linear support vector machine is built up to identify whether a target region is a pothole. Based on this, an algorithm for recognizing the potholes of the pavement is proposed. The experimental results show that the algorithm can achieve a high recognition rate.
Many on-going projects in the field of vehicular networks are working in the direction of providing driver with relevant information about roads and traffic movements. In this paper, we propose a novelWi-Fi based architecture for Pothole Detection and Warning System which assists the driver in avoiding potholes on the roads by giving prior warnings. The system consists of access points placed on the roadsides for broadcasting data, which can be received byte Wi-Fi enabled vehicles as they enter the area covered by the influence of the access points. The mobile nodes can also broadcast their response as feedback which when received by access point can beutilized for backend server processing.The pothole detection application proposed in this paper enables the driver to receive information of the potholes on the roads in the vicinity of the moving vehicle. The application can be integrated in the vehicle so as to alarm the driver in the form of a visual signal, audio signal or even trigger the braking system. Simulations demonstrate the advantages of using our approach for constructing pothole detection systems. Many such similar applications can be deployed over the framework provided by the system to assist navigation on roads.
Potholes and pitfalls spotter
- P More
- S Surendran
- S Mahajan
- S K Dubey
Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection
- R Sundar
- S Hebbar
- V Golla