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Relevant bibliographies by topics / Pothole Detection

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Journal articles
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Academic literature on the topic 'Pothole Detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Pothole Detection"

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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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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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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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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Dissertations / Theses on the topic "Pothole Detection"

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Li, Yaqi. "Road Pothole Detection System Based on Stereo Vision." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1525708920748809.

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Zhang, Dalong. "Road pothole detection method using built-in sensors in smartphone." Thesis, University of Dundee, 2017. https://discovery.dundee.ac.uk/en/studentTheses/ae02e999-acbc-41fc-a092-7bd88d362f20.

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In this thesis, the smartphone is installed on vehicle as a tool to detect potholes on the road. The data collected and pre-processed by smartphone is then analysed to obtain the result. The history and status of pothole detection study is firstly discussed. The traditional pothole detection method has two disadvantages: 1, low efficiency; 2, limited detection area. To work out a new method, the suspension models and tyre models are studied. Also the axis-correction and de-noise in signal process discussed for data analysis. Based on these, the requirement of detection system is analysed, and

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Kharel, Subash. "POTHOLE DETECTION USING DEEP LEARNING AND AREA ASSESSMENT USING IMAGE MANIPULATION." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2825.

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Every year, drivers are spending over 3 billions to repair damage on vehicle caused by potholes. Along with the financial disaster, potholes cause frustration in drivers. Also, with the emerging development of automated vehicles, road safety with automation in mind is being a necessity. Deep Learning techniques offer intelligent alternatives to reduce the loss caused by spotting pothole. The world is connected in such a way that the information can be shared in no time. Using the power of connectivity, we can communicate the information of potholes to other vehicles and also the department of

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Hu, Yazhe. "Degenerate Near-planar Road Surface 3D Reconstruction and Automatic Defects Detection." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98671.

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This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D

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Karuppuswamy, Jaiganesh. "Detection and Avoidance of Simulated Potholes in Autonomous Vehicles in an Unstructured Environment." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990731390.

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Hedenström, Linus, and Sebastian Eriksson. "An investigation of detecting potholes with UAV LiDAR and UAV Photogrammetry." Thesis, Högskolan i Gävle, Samhällsbyggnad, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-36836.

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Potholes are caused by erosion and as such always emerging on our roadnetwork. Potholes may not only cause great damages to vehicles, but can alsocause road accidents, which in the worst case are fatal. Today, the detection ofpotholes is usually based on citizen reports or ocular inspection by vehicle,where a loose description of the potholes properties and location can be given.Recent research has explored the possibility of aerial inspection of paved roadswith the new, cost effective, Structure-from-Motion (SfM) technique, whichcan produce 3D point clouds from photogrammetric data. SfM point

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gupta, Devansh. "Smart-Scooter Rider Assistance System using Internet of Wearable Things and Computer Vision." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1619611136736967.

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ARYA, DEEPRAJ. "POTHOLE DETECTION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20452.

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Roads are the most important form of nation's transportation system. It is extremely crucial to maintain them in good situation. Potholes are a type of road problem that can harm vehicles and have a detrimental impact on drivers' ability to drive safely, which can result in traffic accidents. Potholes that develop on the road must be filled to keep the roadways in excellent condition. It is essential that you keep them in good shape. It can be difficult to locate potholes in the road, particularly in India where there are millions of km of roadways. In a complicated road environmen

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林淳皓. "A Real-Time Pothole Detection Approach using Mobile Sensing Device." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/13846600401194328363.

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碩士<br>國立交通大學<br>資訊管理研究所<br>100<br>In recent years, fast economic growth and rapid technology advance have led to significant impact of the quality of traditional transport system. Intelligent Transportation System (ITS), which aims to improve the transport system, has therefore become more and more popular. The one of the aims of ITS is to enhance traffic safety, the driver is injured mostly due to the potholes on the road in traffic accidents. If it is possible, establish of real-time pothole detection system and share the pothole information with drivers, that will be improve the traffic saf

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Asaduzzaman, Md. "Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness." 2019. https://monarch.qucosa.de/id/qucosa%3A72299.

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In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Proce

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Book chapters on the topic "Pothole Detection"

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Linginani, Indira, Kyatham Hanishka Reddy, Salla Sanjana, Thummala Srithika, and S. R. Pooja. "Pothole Detection Using YOLO v7." In ICT Analysis and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6568-7_36.

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Bhavya, Pathipati, Ch Sharmila, Y. Sai Sadhvi, Ch M. L. Prasanna, and Vithya Ganesan. "Pothole Detection Using Deep Learning." In Smart Technologies in Data Science and Communication. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1773-7_19.

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Cheng, Jintao, Xingming Chen, Weiwen Chen, et al. "Pothole-YOLO: A Single-Stage Instance Segmentation Method for Pothole Detection." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1087-4_43.

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Murali, Gunji Bala, V. Santosh Kumar, Dibya Narayan Behera, Kapil Kumar Mohanta, Omkar Tulankar, and Sanketh S. Salimath. "Pothole Detection on Roads Using Canny Edge Detection Algorithm." In Applications of Computational Methods in Manufacturing and Product Design. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0296-3_60.

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Li, Wei, Xiangwei Zhang, Zhuo Lei, et al. "One Stage Near-Ground Pothole Object Detection." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5612-4_36.

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Islam, Sahil, Hamzah Hamid, Mayurnav Goswami, Nupur Choudhury, Rupesh Mandal, and Gitu Das. "An Edge Computing Framework for Pothole Detection." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3817-5_43.

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Chakraborty, Sanchay, Dakshita Prashar, and S. Sathya Priya. "Real-Time Pothole Detection Using Deep Learning." In Next-Gen Technologies in Computational Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003430452-43.

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Suganya, R., Boya Harshita, Dhana Lakshmi, M. Mithun Kumar Reddy, and Bada Rajasekhar Reddy. "Pothole Detection Using IoT to Help People." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51167-7_73.

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Arulananth, T. S., M. Baskar, K. Thrishma, N. Srilekha, Sai supraja, and Ch Ravalika. "Pothole Detection Using Arduino and Ultrasonic Sensors." In Advanced Techniques for IoT Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4435-1_42.

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Anandhi, C., and Kavitha Rajamohan. "An IoT-Based Model for Pothole Detection." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9012-9_39.

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Conference papers on the topic "Pothole Detection"

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Nanda Kumar, Bvn, Soham Girish Kulkarni, Shlok Kakkar, and Kizheppatt Vipin. "ADAS Pothole Detection System using FPGA." In 2023 IEEE Asia Pacific Conference On Postgraduate Research In Microelectronics And Electronics (PRIMEAsia). IEEE, 2023. http://dx.doi.org/10.1109/primeasia60757.2023.00028.

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R, SUHAS A., GIRIJALAKSHMI, PAVAN T. A, SANDEEP REDDY P, SHREYAS B. S, and MAHENDRA M. "Advanced Pothole Detection Using Image Processing." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627802.

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Pawar, Sahil, Sanket Mahajan, Pushkar Pawar, Pranav Khaire, Suruchi Dedgaonkar, and Priya Shelke. "Road Pothole Detection and Reporting System." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10987950.

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Maji, Anushka, Sagnik Sinha, Subhabrata Banerjee, Amit Kumar Das, Malay Gangopadhyay, and Roshan Rateria. "Designing of Smart Pothole Detection System." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10860139.

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Mosela, David, Moses Olaifa, Pius Owolawi, and Vusi Malele. "Autonomous Social Amenity, Pothole Detection and Reporting." In 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2024. https://doi.org/10.1109/imitec60221.2024.10850895.

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Kumar, V. Bhuvana, N. Yedukondalu, and A. Narayana Rao. "Automated Pothole Detection with Convolutional Neural Networks." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00072.

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Sminage, Amxson, Delvin P. B, Derick Davies, Vivek K. J, and Jasmy Davies. "SafeDrive: Intelligent Pothole Detection and Mapping System." In 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2025. https://doi.org/10.1109/ictest64710.2025.11042541.

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Navalgund, Anirudh, E. Sujaya, Shashank Padavalkar, Royston Vedamuthu, and Ujwala Patil. "LiCam: A Synergistic Approach for Pothole Detection." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039557.

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Joy, Salna, Haripriya P, and Shruti Shukla. "Novel Pothole Detection System Using YOLOv8 Algorithm." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11064167.

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D’Couto, Adam Louis, Maria Juliana David, Parag Jose C, Praghash K, and Yong Wang. "Pothole Detection and Powertrain Control for Vehicular Safety." In 2024 International Conference on Vehicular Technology and Transportation Systems (ICVTTS). IEEE, 2024. http://dx.doi.org/10.1109/icvtts62812.2024.10763932.

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