Smart hardware-integrated deep learning framework for real-time pothole detection in vehicles

Area of Science:

• Engineering
• Computer Science
• Transportation Science

Background:

• Potholes in road infrastructure present significant risks to vehicle safety and increase maintenance expenses.
• Current pothole detection methods, relying on manual inspection or costly sensors, are inefficient and expensive.

Purpose of the Study:

• To develop a cost-effective, automated, and scalable solution for pothole detection and road monitoring.
• To leverage the Internet of Things (IoT) and deep learning for enhanced pothole detection accuracy.

Main Methods:

• An IoT-based system was designed using an ESP8266 microcontroller, ultrasonic sensors, and a camera module for real-time data collection.
• Collected road surface data was processed using deep learning models for accurate pothole identification.
• Wireless data transmission via ESP8266 to a central server enabled further analysis and mapping.

Main Results:

• The integrated system achieved a 95% accuracy in pothole detection.
• The system demonstrated a reduction in manual labor requirements for road monitoring.
• The solution proved to be cost-effective, automated, and scalable for practical road maintenance.

Conclusions:

• The proposed IoT and deep learning system offers a significant advancement in pothole detection technology.
• This approach facilitates proactive road maintenance strategies by providing timely and accurate data.
• The system presents a viable and efficient alternative to traditional methods for road infrastructure monitoring.