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Journal articles on the topic 'Pothole Detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ryu, Seung-Ki, Taehyeong Kim, and Young-Ro Kim. "Feature-Based Pothole Detection in Two-Dimensional Images." Transportation Research Record: Journal of the Transportation Research Board 2528, no. 1 (2015): 9–17. http://dx.doi.org/10.3141/2528-02.

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Accurately detecting potholes is an important task in determining the proper strategies for pavement maintenance and rehabilitation. However, manually detecting and evaluating methods are expensive and time-consuming. A pothole detection method is proposed in this study; the method uses various features in two-dimensional (2-D) images that improve the existing method and can accurately detect a pothole. The proposed method can be divided into three steps: ( a) segmentation, ( b) candidate region extraction, and ( c) decision. First, a histogram and the closing operation of a morphology filter

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Frnda, Jaroslav, Srijita Bandyopadhyay, Michal Pavlicko, Marek Durica, Mihails Savrasovs, and Soumen Banerjee. "Analysis of Pothole Detection Accuracy of Selected Object Detection Models Under Adverse Conditions." Transport and Telecommunication Journal 25, no. 2 (2024): 209–17. http://dx.doi.org/10.2478/ttj-2024-0016.

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Abstract Potholes detection is an essential aspect of road safety and road infrastructure maintenance. Potholes, which are typically caused by a combination of heavy traffic and weather, are depressions or holes in the road surface that can cause damage to specific parts of a vehicle. Autonomous vehicles, in particular, must be capable of detecting and avoiding them. Hitting a deep or sharp-edged pothole at high speed can lead to loss of control or even an accident. This makes pothole detection all the more important. The accuracy of pothole detection systems installed in autonomous vehicles m

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3

Lincy, A., G. Dhanarajan, S. Sanjay Kumar, and B. Gobinath. "Road Pothole Detection System." ITM Web of Conferences 53 (2023): 01008. http://dx.doi.org/10.1051/itmconf/20235301008.

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A pothole is an open crack developed on roads owing to varied climatic situations and exposure to heavy-load trucks. Potholes are acting as one of the major causes of accidents and economic loss in the repair of vehicles. Hence this paper proposes a pothole detection system that assists drivers in avoiding potholes on the road by providing prior warnings using the YOLOV7 machine learning technique. The warning can be like a buzzer while the vehicle approaches a pothole. This concept can be expanded to create vehicles that detect humps and other road irregularities. The application depicted in

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4

Veeraswamy, D. "IOT based Smart Pothole Detection System using ESP32." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40900.

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This project presents a comprehensive pothole detection and navigation system aimed at improving road safety and reducing vehicle wear and tear. The system utilizes an ESP32 microcontroller, a gyroscope, and a GPS module, integrated into car tires to enable real-time detection of potholes. The gyroscope measures vibrations and deviations caused by uneven road surfaces, while the GPS module pinpoints the exact geographic location of the detected potholes. The system also measures the depth and width of the potholes to provide detailed information. This data is transmitted wirelessly to a centra

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Fairuz Mat Radzi, Siti, Mohd Amiruddin Abd Rahman, and Muhammad Luqman Arif Bin Mohamad. "RT-DETR-Pothole: Lightweight Real-Time Detection Transformers for Improved Road Pothole Detection." Journal of Physics: Conference Series 3022, no. 1 (2025): 012003. https://doi.org/10.1088/1742-6596/3022/1/012003.

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Abstract Assessment of on-time road condition is crucial for ensuring the safety of the motorist. One of the recent approaches to detecting road potholes is to analyze images captured from an unmanned aerial vehicle (UAV). Although the traditional deep learning model could perform accurate detection during offline analysis, there is still a limitation of the available algorithms that could perform real-time evaluation. Therefore, this study proposes a lightweight transformer algorithm, the real-time detection transformer (RT-DETR), for online evaluation of road pothole images. The models were

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Raut, Yash. "Pothole Detection and Reporting System." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1945–49. http://dx.doi.org/10.22214/ijraset.2021.37479.

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Abstract: Potholes on roads are the major problem for citizens acting as pedestrians as well as drivers. Government bodies which consist of engineers and workers are responsible to detect damages on roads and fix those damages. A recent study stated that every year around 3,597 people die due to potholes. The size and depth of the pothole may vary in a different place. Potholes had to be taken seriously. This system consists of a citizen with a handheld android/ios device with internet and GPS enabled, gathering the data in form of images and reporting to the government along with Geo-location

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Tushar, Premanand, Sridhar Sriram, and Durbha Abhinav. "Pothole Detection System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 89–92. https://doi.org/10.35940/ijeat.F1281.089620.

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Potholes are a common nuisance that most people have had the displeasure of coming across. These bowl-shaped cavities in the road cause a large proportion of automobile related accidents, either directly or indirectly. Begetting the process of getting a pothole covered/fixed is a time consuming one that involves informing the appropriate authorities and having them take action. Implementing a system that involves citizens in the process of detecting pothole is what is being envisioned. The system includes a mobile application which is capable of taking a photo, this photo is then sent to a bac

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Wang, Penghui, Yongbiao Hu, Yong Dai, and Mingrui Tian. "Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1604130.

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Potholes are one type of pavement surface distresses whose assessment is essential for developing road network maintenance strategies. Existing methods for automatic pothole detection either rely on expensive and high-maintenance equipment or could not segment the pothole accurately. In this paper, an asphalt pavement pothole detection and segmentation method based on energy field is put forward. The proposed method mainly includes two processes. Firstly, the wavelet energy field of the pavement image is constructed to detect the pothole by morphological processing and geometric criterions. Se

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9

Salaudeen, Habeeb, and Erbuğ Çelebi. "Pothole Detection Using Image Enhancement GAN and Object Detection Network." Electronics 11, no. 12 (2022): 1882. http://dx.doi.org/10.3390/electronics11121882.

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Many datasets used to train artificial intelligence systems to recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due to the fact that these datasets present complex but realistic scenarios of pothole detection tasks than popularly used datasets that achieve better results but do not effectively represents realistic pothole detection task. In remote sensing, super-resolution generative adversarial networks (GAN), such as enhanced super-resolution generative adversarial

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Wang, Hsiu-Wen, Chi-Hua Chen, Ding-Yuan Cheng, Chun-Hao Lin, and Chi-Chun Lo. "A Real-Time Pothole Detection Approach for Intelligent Transportation System." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/869627.

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In recent years, fast economic growth and rapid technology advance have led to significant impact on the quality of traditional transport system. Intelligent transportation system (ITS), which aims to improve the transport system, has become more and more popular. Furthermore, improving the safety of traffic is an important issue of ITS, and the pothole on the road causes serious harm to drivers’ safety. Therefore, drivers’ safety may be improved with the establishment of real-time pothole detection system for sharing the pothole information. Moreover, using the mobile device to detect pothole

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Das, B. Tulasi. "POTHOLE DETECTION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31893.

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One of the major problems in developing countries is maintenance of roads. Well maintained roads contribute a major portion to the country's economy. Identification of pavement distress such as potholes and humps not only help drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses previous pothole detection methods that have been developed and proposes a cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Ultrasonic sensors are used to identify

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Sonawane, Nilesh. "Pothole detection and cost estimation - Research Paper." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49076.

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Abstract In this paper, An advanced pothole detection method is proposed in this project with accurate calculation of the dimensions of the potholes, estimation of their volume, and determination of the filling cost of these potholes by using video inputs from road surfaces. Our system uses model object detection models to process every video frame to precisely identify potholes, and then me the dimensions as concerning length, breadth, and depth by using depth estimation model. Specifically, the system applies a technology known as monocular depth estimation, which allows for the estimation o

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Ma, Xinjiang, Dongjie Yue, Siyuan Li, Dongjian Cai, and Yi Zhang. "Road potholes detection from MLS point clouds." Measurement Science and Technology 34, no. 9 (2023): 095017. http://dx.doi.org/10.1088/1361-6501/acdb8d.

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Abstract The extraction of pavement damage information is one of the major difficulties in the application research of mobile laser scanning point cloud data. To address the problem of inaccurate detection results by using only relative distance to detect potholes, this paper proposes a novel pothole detection method that combines directed distance and skewed distribution. Firstly, the rapid positioning of the pothole is realized by the directed distance, which is calculated from the points and the local fitted plane. And monomerization and denoising of potential potholes are achieved by densi

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Gajjar, K., T. van Niekerk, Thomas Wilm, and P. Mercorelli. "Vision-Based Deep Learning Algorithm for Detecting Potholes." Journal of Physics: Conference Series 2162, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2162/1/012019.

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Abstract Potholes on roads pose a major threat to motorists. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of pothole

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Xu, Yi, Teng Sun, Shaohong Ding, et al. "VIDAR-Based Road-Surface-Pothole-Detection Method." Sensors 23, no. 17 (2023): 7468. http://dx.doi.org/10.3390/s23177468.

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This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By compa

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Kim, Young-Mok, Young-Gil Kim, Seung-Yong Son, Soo-Yeon Lim, Bong-Yeol Choi, and Doo-Hyun Choi. "Review of Recent Automated Pothole-Detection Methods." Applied Sciences 12, no. 11 (2022): 5320. http://dx.doi.org/10.3390/app12115320.

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Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and

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M, Nandish. "YOLOv4 BASED POTHOLE DETECTION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34527.

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Potholes are very commonly found these days and they found everywhere. This leads to major disturbances to our society in the form of vehicle damages, accidents and it also effects the economy of that countries where potholes are present. To solve the problem, the solution is created using YOLO (You Only Look Once) algorithm for pothole detection. It is a pretrained model which detects the pothole using YOLO v4 algorithm. In last decade many different techniques are proposed but YOLO gave better results and the speed and accuracy is also high. In the object detection system, the input is given

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Rajesh D. Thakare. "Development of a Probabilistic Framework for Enhanced Vision Safety in Driver Assistance Systems." Communications on Applied Nonlinear Analysis 31, no. 2s (2024): 422–35. http://dx.doi.org/10.52783/cana.v31.658.

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Potholes on roads endanger public safety and infrastructure upkeep. This paper discusses the design and implementation of a real-time pothole detection system that employs a GPS module to locate potholes, a Raspberry Pi, an ESP32 microcontroller, a camera for image processing, and a 16x2 LCD to display pothole detection and GPS locations in real-time. To support successful maintenance, the technology tries to identify potholes in real time, precisely measure their dimensions, and document their locations. The proposed system enhances road safety while also lowering maintenance costs by taking

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Talha, Sk Abu, Dmitry Manasreh, and Munir D. Nazzal. "The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes." Buildings 14, no. 4 (2024): 1078. http://dx.doi.org/10.3390/buildings14041078.

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Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses

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Jakubec, Maroš, Eva Lieskovská, Boris Bučko, and Katarína Zábovská. "Comparison of CNN-Based Models for Pothole Detection in Real-World Adverse Conditions: Overview and Evaluation." Applied Sciences 13, no. 9 (2023): 5810. http://dx.doi.org/10.3390/app13095810.

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Potholes pose a significant problem for road safety and infrastructure. They can cause damage to vehicles and present a risk to pedestrians and cyclists. The ability to detect potholes in real time and with a high level of accuracy, especially under different lighting conditions, is crucial for the safety of road transport participants and the timely repair of these hazards. With the increasing availability of cameras on vehicles and smartphones, there is a growing interest in using computer vision techniques for this task. Convolutional neural networks (CNNs) have shown great potential for ob

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Surahmanto, Muhammad, Suhardi Aras, Muh Rifki Idhan Adhim, and Putri Ussalama. "Deteksi Jalan Berlubang Menggunakan Algoritma Yolov5." Journal of Digital Business and Information Technology 1, no. 1 (2024): 1–8. http://dx.doi.org/10.23971/jobit.v1i1.198.

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The city of Sorong, as one of the largest cities in the Southwest Papua region, is facing serious problems due to potholes in various areas. This problem causes the risk of accidents and vehicle damage, disrupts the mobility of city residents, and hinders sustainable infrastructure development. Therefore, we need a system that is efficient and accurate in detecting potholes quickly. In this study, researchers used the "You Only Look Once" (YOLO v5) method to detect potholes in Sorong City. YOLO v5 is a real-time object detection algorithm that has been proven to have high speed and accuracy in

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Khude, Sonali Ashok. "Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 109–27. http://dx.doi.org/10.22214/ijraset.2024.59712.

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Abstract: Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its spe

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Pawar, Kshitij, Siddhi Jagtap, and Smita Bhoir. "Efficient pothole detection using smartphone sensors." ITM Web of Conferences 32 (2020): 03013. http://dx.doi.org/10.1051/itmconf/20203203013.

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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 a

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Sundar Rao, Dr Mr Syam, Sandu Vamsi, Murikipudi Pradeep Kumar, Ramineni Akash, and Puramesetty Siva Koteswarao. "Pothole Detection Using Yolo and Computer Vision." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–6. https://doi.org/10.55041/isjem02325.

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Road infrastructure is the infrastructural core and has an important function in transportation and economic growth activities. Nevertheless, potholes have a profound effect on the safety of driving, as they lead to vehicle repairs and operating costs, and on the effectiveness of road systems. State-of-the- art pothole detection methods involve either manual inspection of the road or sensor- based methods, which are inefficient, labor-intensive and expensive. Because of this, there is an increasing demand for automatic intelligent systems to identify potholes automatically and effectively in o

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Vernekar, Pratham, Aniruddha Singh, and Dr Kailash Patil. "Pothole and Wet Surface Detection Using Pretrained Models and ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 626–33. http://dx.doi.org/10.22214/ijraset.2023.49489.

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Abstract: Roads contribute significantly to the economy and serve as a transportation platform. Road potholes are a key source of worry in transportation infrastructure. The purpose of this research is to develop an Artificial Intelligence (AI) model for identifying potholes on asphalt pavement surfaces. Image processing techniques from pretrained models such as efficientnet, resnet50, mobilenet and ML models such as random forest, decision tree, SVC, SVM. Several studies have advocated employing computer vision techniques, including as image processing and object identification algorithms, to

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Ling, Min, Quanjun Shi, Xin Zhao, et al. "Nighttime Pothole Detection: A Benchmark." Electronics 13, no. 19 (2024): 3790. http://dx.doi.org/10.3390/electronics13193790.

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In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on

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Jose, Mekha, Joshy Avin, R. Paleri Abishek, Mohan Athul, and Jasim R. M. Ali. "A Review on Contribution and Influence of Artificial Intelligence in Road Safety and Optimal Routing." International Journal on Emerging Research Areas (IJERA) 04, no. 02 (2025): 56–60. https://doi.org/10.5281/zenodo.14669339.

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Pothole detection is crucial for road safety and maintenance, driving research towards automated and efficient detection systems. Traditional methods present limitations: public reporting, while cost-effective, relies on citizen participation and lacks real-time information; vibration-based methods, using accelerometers to detect vehicle vibrations, require driving over potholes. Image/video processing techniques offer a proactive approach by analysing visual data to identify potholes. These methods often leverage computer vision algorithms, 3D scene reconstruction, and machine learning strate

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Baek, Ji-Won, and Kyungyong Chung. "Pothole Classification Model Using Edge Detection in Road Image." Applied Sciences 10, no. 19 (2020): 6662. http://dx.doi.org/10.3390/app10196662.

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In ad

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KAYA, Ömer, and Muhammed Yasin ÇODUR. "Investigating The Relationship Between Vehicle Speed and Pothole Detection by Using Mobile Phone." Afyon Kocatepe University Journal of Sciences and Engineering 24, no. 1 (2024): 228–41. http://dx.doi.org/10.35414/akufemubid.1328778.

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It is known that road pavements are damaged due to time, climatic conditions and construction errors. Considering these damages, the most important road defect that reduces road safety and comfort is potholes. Especially as the width and depth of the pothole increases, driving safety is also endangered. In addition, the locations of these potholes, especially on urban roads, are determined manually in many regions. This process causes delays in the maintenance and repair of the potholes. To this end, the authors plan an in-vehicle integrated system consisting of multiple stages to automaticall

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Park, Sung-Sik, Van-Than Tran, and Dong-Eun Lee. "Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection." Applied Sciences 11, no. 23 (2021): 11229. http://dx.doi.org/10.3390/app112311229.

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Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s

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Pranav, Prashant Kulkarni, Mandar Kulkarni Ketan, Rajkumar Nimbalkar Vivek, and P. Jadhav S. "Android Pothole Detection System Using Deep Learning." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 1482–85. https://doi.org/10.5281/zenodo.7696183.

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In recent years, technology has contributed to improving transportation systems, but the maintenance and upkeep of road networks remain a challenge despite these advancements. Potholes, cracks, and other road defects can lead to accidents, traffic congestion, and expensive repairs. An android pothole detection system that utilizes smartphone sensors and machine learning algorithms has the potential to revolutionize road maintenance and safety. This proposed system will use a smartphone camera to detect potholes and distinguish them from other road irregularities. It will also be integrated wit

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Alsharafi, Ashraf Khaled, and Muhammed Nafis Osman Zahid. "Investigation on a Vision-Based Approch For Smart Pothole Detection Using Deep learning Based on Fast CNN." MEKATRONIKA 5, no. 2 (2023): 87–99. http://dx.doi.org/10.15282/mekatronika.v5i2.9813.

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The quality of road these days are important and roads always dangerous since its filled with potholes and damages which cause a lot of incident and numbers gets more increased in crowded area , this article investigates and compare the performance metrics of different object detection models that utilized the Fast CNN structure in it's backbones , Four processes make up the standard method of pothole detection: data acquisition, data pre-processing, feature extraction, and pothole classification. for the task of pothole detection. The study focuses on the evaluation of YOLOv6n, YOLOv8n, YOLOv

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Aditya, Prakash Devrukhkar, Anand Dethe Aditya, Patel Sugamkumar, and Fulkant Londhe Swapnil. "Artificial Intelligence Powered Pothole Detection, Reporting and Management Solution." International Journal of Innovative Science and Research Technology 7, no. 4 (2022): 271–74. https://doi.org/10.5281/zenodo.6496700.

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Object Detection Potholes are a traffic hazard, endangering the safety of both automobiles and pedestrians. It is one of the leading causes of road accidents and the loss of lives and property in most developing countries. As a response, there is a need to collect and update data on current road conditions on a regular basis so that vehicles may be warned of other routes and the appropriate government department can take urgent action to remove potholes for the benefit of commuters. Using object identification algorithms on photos captured with a smartphone camera is a simple and effective tec

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Rana, Ghazanfar Ali, Syed Muhammad Adnan, Nudrat Nida, Wakeel Ahmad, and Farooq Bilal. "Asphalt Pavement Potholes Localization and Segmentation using Deep RetinaNet and Conditional Random Fields." Vol 3 Issue 5 3, no. 5 (2022): 126–39. http://dx.doi.org/10.33411/ijist/2021030510.

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The main aspect of maintaining the roads and highways' durability and long life is to detect potholes and restore them. A huge number of accidents occur on the roads and highways due to the pothole. It also causes financial loss to vehicle owners by damaging the wheel and flat tire. For the strategies of the road management system and ITS (Intelligent Transportation System) service, it is one of the major tasks to quickly and precisely detect the potholes. To solve this problem, we have proposed a deep learning methodology to automatically detect and segment the pothole region within the aspha

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Das, Abhishek, and Sourav Saha. "A Computer Vision based Framework for Detecting Potholes on Asphalt-Road using Machine Learning Approach." American Journal of Advanced Computing 1, no. 4 (2020): 1–5. http://dx.doi.org/10.15864/ajac.1402.

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This paper proposes a Pothole Detection Framework which may assist the pedestrian in avoiding potholes on the roads by giving prior warnings. The basic idea of this framework is to detect the pothole on asphalt road by analyzing the image of the road-surface. This proposed framework combines image processing techniques with machine learning methods and primarily explores edges, Histogram of Gradients and Local Binary Patterns of an image frame for extracting features to detect the presence of potholes on the road surface. The experimental results indicate promising potential of the proposed fr

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Shafi Ullah Adid, Md. Emon, and Taofica Amrine. "A hybrid approach to detect and classify pothole on Bangladeshi roads using deep learning." International Journal of Science and Research Archive 12, no. 1 (2024): 1045–53. http://dx.doi.org/10.30574/ijsra.2024.12.1.0950.

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Potholes are a major problem for Bangladesh, a country with a developing economy and infrastructure. Traditional pothole detection methods, often based on manual inspection, are insufficient for effectively managing the vast national road network. The primary focus of this study is on pothole detection on roads in Bangladesh, employing deep learning techniques. The pothole dataset, comprising images captured by us, has been curated, leading to the development of two distinct datasets: one for pothole classification, totaling 6000 images, and another for pothole detection, comprising 1300 image

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Reddy, Greeshma, Brunda V, Bhoomika G S, Bhavana P, and Suma S. "Pothole Detection Using Deep Learning." International Journal of Innovative Research in Advanced Engineering 10, no. 05 (2023): 207–10. http://dx.doi.org/10.26562/ijirae.2023.v1005.12.

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Particularly in poor nations, potholes are a nuisance that frequently cause car damage or danger to the people inside of moving vehicles. The potholes are holes on roadway surface that can cause damage to vehicles which can cause accidents. Poor road conditions are not just a public nuisance because that causes discomfort to drivers, passengers, as well as damage to vehicles which leads to accidents. The pothole detection project helps drivers avoid potholes on a given route by providing advance alerts of their presence. Create a piece of software that can locate potholes is the plan. The conc

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Khambadkar, Prasad. "Speed Breaker and Potholes Detection Using DL and IoT." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1245–52. http://dx.doi.org/10.22214/ijraset.2024.65314.

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This project introduces a novel approach to enhance road safety by implementing a real-time pothole detection system. The system utilizes a camera module to capture video input, which is then processed using a Python file for Image detection. Upon detecting a pothole or a speed breaker, the system triggers a buzzer to alert nearby vehicles and pedestrians. This alert mechanism aims to reduce the risk of accidents and vehicle damage caused by potholes, thereby improving overall road safety. Additionally, the project can contribute to the efficient maintenance of roads by enabling timely repair

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Jenefa, Archpaul, Antony Taurshia, Bessy Kuriakose, Edward Kumar, and Archpaul Lincy. "Advancing road maintenance with EfficientDet-based pothole monitoring." Serbian Journal of Electrical Engineering 22, no. 1 (2025): 57–74. https://doi.org/10.2298/sjee2501057j.

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Effective road maintenance is crucial for ensuring safe and efficient transportation but is often compromised by the widespread occurrence of potholes. This study introduces a novel approach using an EfficientDet-based model for sophisticated pothole monitoring. Potholes pose a significant hazard that requires proactive detection and timely resolution. Traditional detection methods frequently fall short in terms of accuracy and real-time capability. Addressing these limitations, our research leverages the EfficientDet architecture, known for its optimal balance of accuracy and computational ef

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P, Mrs Smitha, Manya K N, Megha P, Monika O G, and Vaishnavi R. "RASTE MITRA: AUTOMATED POTHOLES DETECTION AND FILLING SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem39856.

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Potholes are one of the major issues on the roads because they cause damage to vehicles, lead to accidents, and incur higher maintenance costs. “Raste Mitra” in contrast is a comprehensive solution as it performs automatic pothole detection and classification, as well as repairs. The system employs a Raspberry Pi for controlling the functions, YOLOv5 for detecting potholes in movement, and ultrasonic sensors for object detection. The technique is sustainable due to the use of a solar powered battery management system and autonomous and self-filling technique eliminates need of labor. Additiona

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Tejitha, Dandu, Mutyala Karthik, Kolli Sree Harshitha, Regeti Krishna Chaitanya, and T. Bharath Kumar. "Real-Time Pothole Detection and Audio Warning System for Drivers." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43378.

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Potholes in roads creates a dangerous situation where vehicles get damaged and accidents happen. Manual detection methods are traditional, ineffective, laborious, and inefficient, requiring automation. In this paper we introduce a real-time pothole detection system and audio warning for drivers using YOLOv8, an advanced state of the art deep learning model designed for object detection. This system takes road pictures using a camera, processes them using the trained YOLOv8 model, and detects potholes in real time. Once detected, it produces only an audio alert in case of collision, so as to al

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Kenechukwu Sylvanus Anigbogu, Samuel Ochai Audu-war, Tochukwu Sunday Belonwu, Okwuchukwu Ejike Chukwuogo, and Emmanuel Chibogu Asogwa. "Computer vision for asphalt cracks detction using YOLOv5." International Journal of Science and Research Archive 10, no. 1 (2023): 163–79. http://dx.doi.org/10.30574/ijsra.2023.10.1.0693.

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Recent studies have shown that researchers have proposed various techniques for Pothole detection using data collected from different parts of the world. Automating pothole detection will go a long way in providing safe driving for road users and intelligent transportation systems. This is not only necessary to guarantee safe and adequate performance, but also to adjust to the drivers’ needs, potentiate their acceptability, and ultimately meet drivers’ preferences in bad roads. This paper presents a computer vision model that assists drivers by detecting and predicting potholes while on the ro

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Alzamzami, Ohoud, Amal Babour, Waad Baalawi, and Lama Al Khuzayem. "PDS-UAV: A Deep Learning-Based Pothole Detection System Using Unmanned Aerial Vehicle Images." Sustainability 16, no. 21 (2024): 9168. http://dx.doi.org/10.3390/su16219168.

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Smart cities utilize advanced technologies to enhance quality of life by improving urban services, infrastructure, and environmental sustainability. Effective pothole detection and repair strategies are essential for improving quality of life as they directly impact the comfort and safety of road users. In addition to causing serious harm to residents’ lives, potholes can also cause costly vehicle damage. In this study, a pothole detection system utilizing unmanned aerial vehicles, called PDS-UAV, is developed. The system aids in automatically detecting potholes using deep learning techniques

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Mukesh Kumar Tripathi, Et al. "Detection of Pothole by Applying Convolutional Neural Network and Random Forest Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 401–9. http://dx.doi.org/10.17762/ijritcc.v11i9.8821.

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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 mea

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Pei, Yu Long, Cheng Yuan Mao, and Mo Song. "Driving Mode at Pothole-Subsidence Pavement Based on Wheel Path." Advanced Materials Research 524-527 (May 2012): 847–51. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.847.

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Considering the fact that the forms of asphalt pavement potholes, subsidence and cement pavement potholes (collectively defined as pavement pothole-subsidence) are similar and they can influence traffic flow significantly, we put forward to use indexes such as Tangential Diameter Length, Normal Diameter Length, Depth, Lateral distance, etc to describe the characteristics of pothole-subsidence, and we also adopt AutoScope-2004 video detection system aided by artificial judging to investigate in the surveyed road section. According to different wheel paths, driving modes was classified into thre

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Singh, Anshul. "POTHOLE DETECTION SYSTEM USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30382.

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Potholes represent a ubiquitous road hazard, posing risks to both vehicular safety and infrastructure integrity. Addressing this challenge requires efficient and automated detection systems. Leveraging advancements in machine learning (ML), this paper proposes a pothole detection system using ML techniques. The system employs feature extraction, convolutional neural networks, transfer learning, and semantic segmentation for robust and accurate detection of potholes from image data. The proposed system contributes to the automation of road maintenance processes, enabling timely repairs and enha

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Dhingra, Mayank, Rahul Dhingra, and Meghna Sharma. "Pothole Detection Using Machine Learning Models." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 94–105. http://dx.doi.org/10.32628/ijsrset241126.

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Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into t

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Nomqupu, Sandisiwe, Athule Sali, Adolph Nyamugama, and Naledzani Ndou. "Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor." Applied Sciences 14, no. 7 (2024): 2670. http://dx.doi.org/10.3390/app14072670.

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This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Ent

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Heo, Dong-Hoe, Ji-Yoon Choi, Sang-Baeg Kim, Tae-Oh Tak, and Sheng-Peng Zhang. "Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment." Electronics 12, no. 4 (2023): 826. http://dx.doi.org/10.3390/electronics12040826.

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Potholes on road surfaces pose a serious hazard to vehicles and passengers due to the difficulty detecting them and the short response time. Therefore, many government agencies are applying various pothole-detection algorithms for road maintenance. However, current methods based on object detection are unclear in terms of real-time detection when using low-spec hardware systems. In this study, the SPFPN-YOLOv4 tiny was developed by combining spatial pyramid pooling and feature pyramid network with CSPDarknet53-tiny. A total of 2665 datasets were obtained via data augmentation, such as gamma re

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Patil, Avila, and Vandana Japtap. "Enhancing Urban Road Safety: Pothole Detection Using YOLO." Computer Science, Engineering and Technology 2, no. 3 (2024): 36–43. http://dx.doi.org/10.46632/cset/2/3/5.

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Potholes are a major safety concern on roads as they often lead to accidents. Identifying them promptly is vital in preventing accidents. This research focuses on potholes that are very evident during the rainy season because These road defects pose great difficulties for drivers. This study presents the creation of an automatic pothole segmentation model for real time road damage assessment. Potholes have severe safety implications and infrastructure problems, which indicate a need for effective monitoring and maintenance strategies. A YOLOv8based segmentation model was trained using computer

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