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ProjectsIEEE ProjectsAI-Powered Citizen Complaint Management System Using Deep Learning
AI-Powered Citizen Complaint Management System Using Deep Learning - 1
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IEEE Projects

AI-Powered Citizen Complaint Management System Using Deep Learning

Build an AI-Powered Citizen Complaint Management System using Deep Learning and Flask. The system automatically classifies civic issues from uploaded images, enables complaint tracking, provides an admin dashboard, real-time notifications, and status management. Ideal for Final Year Engineering, B.Tech, BE, BCA, MCA, CSE, IT, and AI & ML students.

computer-science-engineering, information-technology, artificial-intelligence-machine-learning, mca, bca11 Tech
PythonFlaskYOLOv5PyTorchOpenCV+6
₹3,499₹7,99956% OFF
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Inquire via WhatsApp
Secure checkout via Razorpay. Instant download after purchase.
DescriptionFeaturesModulesFAQ

About This Project

AI-Powered Citizen Complaint Management System Using YOLOv5 The AI-Powered Citizen Complaint Management System is an intelligent web application that automates the process of reporting, detecting, classifying, and managing civic issues using the YOLOv5 object detection model. Citizens can upload images of public infrastructure problems, and the system automatically detects and classifies different types of civic issues, helping authorities respond more efficiently. The application provides a secure platform where users can register, log in, submit complaints with images, track complaint status, and communicate with administrators. When a complaint image is uploaded, the YOLOv5 model analyzes the image, identifies the detected civic issue, generates confidence scores, and stores the detection results for further processing. The system includes a comprehensive administrative dashboard that allows authorities to review complaints, update resolution status, manage users, monitor complaint progress, and provide real-time support through an integrated chat system. Email notifications ensure administrators are alerted whenever new support requests are received. The project is designed for Smart City applications, Municipal Corporations, Government Organizations, and E-Governance platforms to reduce manual complaint processing and improve public service efficiency.

Software Details

Category
IEEE Projects
Department
computer-science-engineering, information-technology, artificial-intelligence-machine-learning, mca, bca
Demo
Available
Rating
4.6 (12 reviews)

Project Timeline

Published

7 August 2026

Students Enrolled

150

Chat on WhatsApp

Quick reply guaranteed

Chat Now+91 89039 00752

Key Features

AI-Powered Complaint Detection
YOLOv5 Object Detection
Automatic Civic Issue Classification
Image Upload & Analysis
Citizen Registration & Login
Complaint Tracking System
Admin Dashboard
User Management
Live Chat Support

Technologies Used

PythonFlaskYOLOv5PyTorchOpenCVHTMLCSSJavaScriptBootstrapSQLiteFlask-SocketIO

Project Modules

User Authentication

Complete user authentication module with full implementation

Citizen Dashboard

Complete citizen dashboard module with full implementation

Admin Dashboard

Complete admin dashboard module with full implementation

Complaint Management

Complete complaint management module with full implementation

AI Detection Module (YOLOv5)

Complete ai detection module (yolov5) module with full implementation

Image Upload Module

Complete image upload module module with full implementation

Complaint Tracking

Complete complaint tracking module with full implementation

User Management

Complete user management module with full implementation

Real-Time Chat Module

Complete real-time chat module module with full implementation

Email Notification Module

Complete email notification module module with full implementation

Hardware Requirements

Processor

Intel Core i5 (8th Gen) / AMD Ryzen 5 or higher

RAM

8 GB Minimum (16 GB Recommended)

Storage

20 GB Free Disk Space

GPU

NVIDIA GPU (Optional, recommended for YOLOv5 training)

Internet

Required for package installation and updates

Software Requirements

Operating System

Windows 10/11, Ubuntu 20.04+, or macOS

Python

Python 3.10 or later

PyTorch

Latest Stable Version

YOLOv5

Ultralytics YOLOv5

OpenCV

Latest Version

SQLite

SQLite 3

Visual Studio Code

Latest Version

Web Browser

Google Chrome, Microsoft Edge, or Mozilla Firefox

Frequently Asked Questions

What is the AI-Powered Citizen Complaint Management System?

The AI-Powered Citizen Complaint Management System is a web-based application that uses the YOLOv5 object detection model to automatically detect and classify civic issues from uploaded images. It helps citizens report problems and enables authorities to manage and resolve complaints efficiently.

Which technologies are used in this project?

This project is developed using Python, Flask, YOLOv5, PyTorch, OpenCV, HTML, CSS, JavaScript, SQLite, Flask-SocketIO, and SMTP for email notifications.

What types of civic issues can the system detect?

The AI model can detect and classify seven types of civic issues: Broken Streetlight, Damaged Footpath/Sidewalk, Flood, Garbage, Open Manhole, Sewage Leak, and Water Leakage.

Is this project suitable for Final Year Engineering students?

Yes. This project is suitable for Final Year Engineering, B.Tech, BE, BCA, MCA, Computer Science, Information Technology, and Artificial Intelligence & Machine Learning students who want to build a real-world AI-powered web application.

What features are included in this project?

The project includes user registration and login, complaint image upload, AI-based complaint detection, complaint tracking, admin dashboard, user management, live chat support, email notifications, and complaint status management.

Student Reviews

“As an MCA student, I needed a full-stack project. CodeSelf delivered an excellent web application with proper documentation and live demo.”

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Anjali Mehta

MCA, COEP Pune

“Great support and quality projects. The Raspberry Pi project worked perfectly and the documentation was thorough. Highly recommended.”

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Vikram Singh

EEE, Delhi Technological University

“The IEEE project I ordered was exactly what I needed. The team helped me understand the implementation and I scored well in my viva.”

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Sneha Reddy

CSE, VIT Vellore

“Best project center for engineering students. The MERN stack project I purchased came with clean code, proper documentation, and modifications were done for free.”

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IT, PSG College of Technology

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Priya Patel

ECE, RV College of Engineering

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CSE, BMS College of Engineering

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