DmPanf / Potholes_Detector

Apache License 2.0
4 stars 1 forks source link
opencv-python pothole-detection yolov8

Pothole Detector Project

Road maintenance is a critical aspect of urban management, directly influencing safety and comfort for all traffic participants. One of the common issues that plague drivers, cyclists, and even pedestrians are potholes. Potholes can cause accidents, damage vehicles, and even impede emergency services. Addressing this issue promptly is essential for a safe and efficient transportation system.


The goal is straightforward and quantifiable: to develop a multi-agents system capable of detecting potholes in real time with a high degree of accuracy and with a sufficiently high processing speed, marking detected potholes as dangerous or relatively safe by their width in the image. This system should be capable of being deployed in a vehicle or roadside monitoring equipment to identify and report the location of potholes to a central database.

To achieve this goal, we need to accomplish several tasks:

  1. Collect and curate a dataset of road images that include various pothole conditions.

  2. Train the YOLOv8 model to recognize and accurately pinpoint potholes in these images.

  3. Develop a FastAPI backend capable of receiving image data from the detection system, processing it, and updating the central database in real-time.

  4. Carry out the necessary testing to ensure that the system is reliable and can work under different lighting and weather conditions.

  5. Show the ability to manage the system via Telegram bot and serve various clients such as other bots, web applications or simple clients connected to the server through a single API.

image

Using a multi-agent system for pothole detection is smart because:

By fulfilling these tasks, we aim to create a tool that not only improves road conditions but also enhances public safety and potentially saves on long-term road maintenance costs.

In each case, these agents work independently but contribute to a central system that collects, analyzes, and responds to the data on potholes, which helps the city fix roads faster and more efficiently. This can lead to safer driving, fewer accidents, and happy citizens.


Use Cases

Here's how and where we could use a multi-agent system for detecting potholes:


Main Components of FastAPI:

image

Conclusion

When reflecting on a project like the Pothole Detection using YOLOv8 and FastAPI, it's important to consider both the successes and challenges, as well as the insights gained and future plans.

Successful Implementations


Challenges and Unsuccessful Attempts

Insights Gained

Future Plans and Developments

In summary, while we have achieved key milestones in pothole detection and user interaction, we acknowledge the challenges faced, particularly in model consistency and system scalability. The insights gained from this project are invaluable and will significantly shape our future endeavors to enhance the system's effectiveness and expand its capabilities.

image

Links