The problem Pothole detections solves

Title: Revolutionizing Road Safety: Real-Time Pothole Detection and Reporting System

In an era of technological advancements, a team is pioneering a revolutionary solution. They're developing a vehicle attachment, comprising a camera and a Raspberry Pi 4 microcontroller board. This cutting-edge system employs computer vision (CV) and YOLO (You Only Look Once) to detect potholes in real time, gathering critical data for prompt transmission to government authorities.

At the core of this innovation is a robust web framework. The linchpin of this framework is a dynamic website with features designed to enhance road safety and public transparency.

Key features include:

1. Real-time Pothole Detection: The system utilizes a camera and Raspberry Pi 4 to achieve real-time pothole detection, capturing data on pothole location, size, depth, and more.

2. Interactive Map: The website features an interactive map that displays pothole locations between designated points, offering users insights into road conditions.

3. Detailed Pothole Information: Clicking on a pothole marker on the map redirects users to a dedicated page with comprehensive details, including location, measurements, photographs, and government-provided progress reports.

4. Government Login: This secure login enables concerned authorities directly involved in road construction to update progress reports, ensuring accurate and up-to-date information.

This project promotes transparency and road safety, empowering users with valuable data to make informed travel decisions. It fosters accountability, enhances infrastructure maintenance, and sets a new standard for data-driven decision-making in our communities.

Challenges we ran into

Real-time image processing and object detection are computationally intensive tasks, and Raspberry Pi 4 has limitations in terms of processing power. This led to performance issues, including a significant lag in pothole detection and data processing.
To address this issue, we implemented several optimization techniques:

Algorithm Optimization: We fine-tuned the YOLO model to reduce the computational load while maintaining accuracy. This involved selecting a smaller YOLO model and optimizing model hyperparameters.

Parallel Processing: We leveraged multi-threading and multi-processing techniques to distribute the processing load across multiple CPU cores. This allowed us to process multiple frames concurrently, significantly improving real-time performance.

Hardware Acceleration: We explored the use of hardware acceleration techniques, such as using a GPU or Neural Compute Stick, to offload some of the processing tasks from the Raspberry Pi's CPU.

Challenge: Web Framework Compatibility
Integrating the real-time data from the Raspberry Pi into the web framework proved to be challenging. Ensuring that the website displayed updated information in real-time posed a significant challenge.

Solution: WebSocket Integration
To overcome this hurdle, we integrated WebSocket communication between the Raspberry Pi and the web server. This allowed for seamless, bidirectional communication between the devices, ensuring that new pothole data was instantly pushed to the website. This implementation provided users with real-time updates on potholes and their status.

Challenge: Government Login Security
Implementing a secure government login system raised concerns about data security and unauthorized access.