Authors :
Rashmi C. G.; Mrunali G. M.; Meghana H. Huvanur; Punya P. Y.; Pawanraj S. P.
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/2kftarbt
DOI :
https://doi.org/10.38124/ijisrt/26sep1065
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
There is a lot of work to do in cities across India when it comes to fixing problems like potholes, garbage, broken streetlights and blocked drains. Now most of the work is done by hand. This can be slow. It is hard to keep track of the problems. It is hard to say who is responsible. It is hard to let people know what is happening. To fix these problems this paper introduces SwachhSeva. SwachhSeva is a way to report and manage city problems. It connects people with workers through a mobile app and a website.The system has an app for people. It uses React Native. There is a website for workers. It uses React. There is an end that uses Flask. People can send pictures and their location when they report a problem. Workers can see the reports. They can sort them. They can give them to the person. They can fix them. A special model called MobileNetV2 helps sort the problems. It uses a method called transfer learning. If the model is not sure the problem goes to a person instead.The system checks for repeated reports. It uses a formula called Haversine. This helps people see if others have already reported the problem. They can upvote of sending the same report again. A system gives scores to problems that need more attention. It looks at how many people upvoted. It looks at how old the problem's. It looks at deadlines. This helps workers know which problems to fix first. The system also sends problems to the department. It keeps track of deadlines. A map shows where the problems are. This helps bosses see where the most problems are. It helps them decide where to send help.Testing the model worked well on the data used.. The results depend on how much data there is. SwachhSeva is a way to handle city problems. It is not expensive. It can be better, with real data and better maps.
Keywords :
Civic Complaint Management, MobileNetV2, Transfer Learning, Image Classification, Duplicate Detection, Geolocation, SLA Management, Citizen Grievance Redressal, Human-in-the-Loop AI , Priority Scoring.
References :
- K. S. N. Gowtham, V. Munduri, M. V. Deepak, M. P. L. P. D. Sravanthi, M. A. P. Varma, and S. Bonthu, “A Machine Learning Integrated Framework for Automated Civic Complaint Validation and Prioritization in Smart City Governance,” in *Proc. 4th Int. Conf. on Self Sustainable Artificial Intelligence Systems (ICSSAS)*, 2026,p.1071, doi: 10.1109/ICSSAS68835.2026.11559493.
- P. K. S., S. Srividhya, S. Vishakh, and V. B. R., “CivicEye: AI and Blockchain-Powered Platform for Transparent Civic Issue Reporting and Resolution,” in *2026 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)*, 2026, doi: 10.1109/IITCEE67948.2026.11394376.
- M. S. A., S. K., and N. K., “Smart Complaint Routing Systems for Public Issues Using Multimodal Artificial Intelligence,” in *2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON)*, Lonavala, India, 2026, doi: 10.1109/I3CTCON68242.2026.11508062.
- V. Bhatia, A. K. Pathak, A. K. Singh, S. Raghuwanshi, P. Kumar, and O. Krejcar, “Real-time pothole detection using YOLO models: An efficient and cost-effective solution for infrastructure monitoring,” *International Journal of Transportation Science and Technology*, 2026, doi: 10.1016/j.ijtst.2025.12.004.
- S. R. Kuthyar, S. Roopashree, V. Rasika, S. Manjesh, R. Girimaji, R. D. Arjun, and S. Priyadarshini, “An Intelligent Pothole Detection and Alerting System using Mobile Sensors and Deep Learning,” in *2021 IEEE 18th India Council International Conference (INDICON)*, Guwahati, India, 2021, pp. 1–6, doi: 10.1109/INDICON52576.2021.9691661.
- G. Babu R., C. Chellaswamy, M. Surya Bhupal Rao, M. Saravanan, E. Kanchana, and J. Shalini, “Deep Learning Based Pothole Detection and Reporting System,” in *2020 7th International Conference on Smart Structures and Systems (ICSSS)*, Chennai, India, 2020, pp. 143–148, doi: 10.1109/ICSSS49621.2020.9202061.
- H. Abdu and M. H. M. Noor, “A Survey on Waste Detection and Classification Using Deep Learning,” *IEEE Access*, vol. 10, pp. 128151–128165, 2022, doi: 10.1109/ACCESS.2022.3226682.
- H. Ma, Y. Ye, J. Dong, and Y. Bo, “An Intelligent Garbage Classification System Using a Lightweight Network MobileNetV2,” in *2022 7th International Conference on Signal and Image Processing (ICSIP)*, Suzhou, China, 2022, pp. 531–535, doi: 10.1109/ICSIP55141.2022.9886985.
- K. K. Sai, D. D. V. Kumar, A. Sahrudhay, and K. Dharavath, “Pothole Detection Using Deep Learning,” in *2023 2nd International Conference on Futuristic Technologies (INCOFT)*, Belagavi, Karnataka, India, 2023, pp. 1–6, doi: 10.1109/INCOFT60753.2023.10425461.
- S. Bej, S. Roy, D. Daw, A. Paul, S. Saha, S. Maity, and N. Ghosh, “SmartPave: An Advanced IoT-Based System for Real-Time Pothole Detection, Tracking, and Maintenance,” in *2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)*, Bhubaneswar, India, 2023, pp. 532–537, doi: 10.1109/APSIT58554.2023.10201720.
- S. Alshammari and S. Song, “3Pod: Federated Learning-Based 3 Dimensional Pothole Detection for Smart Transportation,” in *2022 IEEE International Smart Cities Conference (ISC2)*, Pafos, Cyprus, 2022, pp. 1–7, doi: 10.1109/ISC255366.2022.9922195.
- I. Isha, S. Bhardwaj, S. Kumari, U. Srivastva, et al., “Analysing Urban Grievances on X (Formerly Twitter) Using NLP: A Framework for Civic Action,” in *2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA)*, Greater Noida, India, 2025, doi: 10.1109/ICCCA66364.2025.11325672.
- B. Kabilan, R. Sairam, and M. Praveen, “Enhanced CNN Architecture for Accurate Waste Classification in Smart Cities,” in Proc. 2024 Int. Conf. IoT Based Control Networks and Intell. Syst. (ICICNIS), Bengaluru, India, 2024, pp. 538–543, doi: 10.1109/ICICNIS64247.2024.10823383.
- D. Jyothirmai, S. Sai Charan Reddy, N. Sai Karthikeya, S. Dinesh Reddy, P. Jagadeesh, and R. Pitchai, “Pothole Detection and Enhanced Road Safety Using Machine Learning,” in Proc. 2024 5th Int. Conf. Electron. Sustainable Commun. Syst. (ICESC), Coimbatore, India, 2024, pp. 1392–1396, doi: 10.1109/ICESC60852.2024.10690147.
There is a lot of work to do in cities across India when it comes to fixing problems like potholes, garbage, broken streetlights and blocked drains. Now most of the work is done by hand. This can be slow. It is hard to keep track of the problems. It is hard to say who is responsible. It is hard to let people know what is happening. To fix these problems this paper introduces SwachhSeva. SwachhSeva is a way to report and manage city problems. It connects people with workers through a mobile app and a website.The system has an app for people. It uses React Native. There is a website for workers. It uses React. There is an end that uses Flask. People can send pictures and their location when they report a problem. Workers can see the reports. They can sort them. They can give them to the person. They can fix them. A special model called MobileNetV2 helps sort the problems. It uses a method called transfer learning. If the model is not sure the problem goes to a person instead.The system checks for repeated reports. It uses a formula called Haversine. This helps people see if others have already reported the problem. They can upvote of sending the same report again. A system gives scores to problems that need more attention. It looks at how many people upvoted. It looks at how old the problem's. It looks at deadlines. This helps workers know which problems to fix first. The system also sends problems to the department. It keeps track of deadlines. A map shows where the problems are. This helps bosses see where the most problems are. It helps them decide where to send help.Testing the model worked well on the data used.. The results depend on how much data there is. SwachhSeva is a way to handle city problems. It is not expensive. It can be better, with real data and better maps.
Keywords :
Civic Complaint Management, MobileNetV2, Transfer Learning, Image Classification, Duplicate Detection, Geolocation, SLA Management, Citizen Grievance Redressal, Human-in-the-Loop AI , Priority Scoring.