SafeRoute Scanner: A Comprehensive System for Pothole Detection, Traffic Sign Recognition, Mapping, and Automated Reporting to Local Authorities
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 5 |
| Year of Publication : 2024 |
| Authors : Ajitkumar Shitole, Shraddha Patil, Vaishnavi Gohad, Gauri Khanapure, Abhishek Mahajan |
10.14445/23488379/IJEEE-V11I5P116
How to Cite?
Ajitkumar Shitole, Shraddha Patil, Vaishnavi Gohad, Gauri Khanapure, Abhishek Mahajan, "SafeRoute Scanner: A Comprehensive System for Pothole Detection, Traffic Sign Recognition, Mapping, and Automated Reporting to Local Authorities," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 5, pp. 175-187, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I5P116
Abstract:
The frequency of road accidents is on the rise with each passing day, with one of the leading causes being drivers’ unawareness of traffic signs and damaged roads due to the presence of potholes. This paper introduces a web application that helps drivers make better driving decisions by warning them of potholes and traffic signs. The application maps detected potholes on Google Maps, providing alerts to other users travelling on the same road. Additionally, it reports the detected pothole locations to local authorities for repair. This approach utilizes the YOLOv8 algorithm for traffic sign and pothole detection. The proposed system aims to enhance driver safety while also engaging users in assisting local authorities with road repairs, which is cost-effective and less time-consuming.
Keywords:
Mapping, Potholes, Traffic Signs, Voice alerts, Yolov8.
References:
[1] Satish Kumar Satti et al., “Unified Approach for Detecting Traffic Signs and Potholes on Indian Roads,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9745-9756, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Vijay Kumar et al., “Driving to Safety: Real-Time Danger Spot and Drowsiness Monitoring System,” Soft Computing, vol. 25, pp. 1447914497, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vivek Srivastava, Sumita Mishra, and Nishu Gupta, “Automatic Detection and Categorization of Road Traffic Signs Using a KnowledgeAssisted Method,” Procedia Computer Science, vol. 218, pp. 1280-1287, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hanxiao Rong et al., “Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm,” IEEE Access, vol. 8, pp. 171129-171139, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Wenjie Yang et al., “Deformable Convolution and Coordinate Attention for Fast Cattle Detection,” Computers and Electronics in Agriculture, vol. 211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Prabhat Singh et al., “Road Pothole Detection from Smartphone Sensor Data Using Improved LSTM,” Multimedia Tools and Applications, vol. 83, pp. 26009-26030, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ronghua Du et al., “Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor,” Sensors, vol. 20, no. 2, pp. 1-17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] B. Mohan Prakash, and K.C. Sriharipriya, “Enhanced Pothole Detection System Using YOLOX Algorithm,” Autonomous Intelligent Systems, vol. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] J. Baek, and K. Chung, “Pothole Classification Model Using Edge Detection in Road Image,” Applied Sciences, vol. 10, no. 19, pp. 1-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Kashish Bansal et al., “DeepBus: Machine Learning Based Real-Time Pothole Detection System for Smart Transportation Using IoT,” Internet Technology Letters, vol. 3, no. 3, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Susmita Patra, Asif Iqbal Middya, and Sarbani Roy, “PotSpot: Participatory Sensing Based Monitoring System for Pothole Detection Using Deep Learning,” Multimedia Tools and Applications, vol. 80, pp. 25171-25195, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Zahid Hasan et al., “Pothole and Speed Breaker Detection Using Smartphone Cameras and Convolutional Neural Network,” 2020 IEEE Region 10 Symposium (TENSYMP), Bangladesh, pp. 279-282, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Abhishek Kumar; “A Modern Pothole Detection Technique Using Deep Learning,” 2nd International Conference on Data, Engineering and Applications (IDEA), India, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] David Mijic et al., “Traffic Sign Detection Using YOLOv3,” 2020 IEEE 10th International Conference on Consumer Electronics (ICCEBerlin), Germany, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] R. Kavitha, and S. Nivetha, “Pothole and Object Detection for an Autonomous Vehicle Using YOLO,” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), India, pp. 1585-1589, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mallikarjun Anandhalli et al., “Indian Pothole Detection Based on CNN and Anchor-Based Deep Learning Method,” International Journal of Information Technology, vol. 14, pp. 3343-3353, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Braian Varona, Ariel Monteserin, and Alfredo Teyseyre, “A Deep Learning Approach to Automatic Road Surface Monitoring and Pothole Detection,” Personal and Ubiquitous Computing, vol. 24, pp. 519-534, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Savita Chougule, and Alka Barhatte, “Smart Pothole Detection System Using Deep Learning Algorithms,” International Journal of Intelligent Transportation Systems Research, vol. 21, pp. 483-492, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yanzhao Zhu, and Wei Qi Yan, “Traffic Sign Recognition Based on Deep Learning”, Multimedia Tools and Applications, vol. 81, pp. 17779-17791, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] U. Sirisha et al., “Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection,” International Journal of Computational Intelligence Systems, vol. 16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Tausif Diwan, G. Anirudh, and Jitendra V. Tembhurne, “Object Detection Using YOLO: Challenges, Architectural Successors, Datasets and Applications,” Multimedia Tools and Applications, vol. 82, pp. 9243-9275, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Road Accidents in India 2022, The Ministry of Road Transport and Highways (Transport Research Wing), Government of India. [Online]. Available: https://morth.nic.in/sites/default/files/RA_2022_30_Oct.pdf
[23] R. Ganesh Babu et al., “Deep Learning Based Pothole Detection and Reporting System,” 2020 7th International Conference on Smart Structures and Systems (ICSSS), India, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Ping Ping, Xiaohui Yang, and Zeyu Gao, “A Deep Learning Approach for Street Pothole Detection,” 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (Big Data Service), UK, pp. 198-204, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Rahul Dhingra, Mayank Dhingra, and Meghna Sharma, “Pothole Detection Using Machine Learning Models,” International Journal of Scientific Research in Science, Engineering and Technology, vol. 11, no. 2, pp. 94-105, 2024.
[CrossRef] [Publisher Link]
[26] Rohan Borgalli, “Smart Pothole Detection and Mapping System,” Journal of Ubiquitous Computing and Communication Technologies, vol. 2, no. 3, pp. 136-144, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Etukala Jaswanth Reddy et al., “Development and Analysis of Pothole detection and Alert based on NodeMCU,” 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), India, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Oche Alexander Egaji et al., “Real-Time Machine Learning-Based Approach for Pothole Detection,” Expert Systems with Applications, vol. 184, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Yeoh Keng Yik et al., “A Real-Time Pothole Detection Based on Deep Learning Approach,” Journal of Physics: Conference Series, vol. 1828, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Pranjal A. Chitale et al., “Pothole Detection and Dimension Estimation System Using Deep Learning (YOLO) and Image Processing,” 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), New Zealand, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]