Work

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

Year: 2026

Type: article

Abstract: Road infrastructure plays a crucial role in transportation, and Potholes pose a significant threat to vehicle safety and maintenance costs. Traditional Pothole detection methods rely on manual inspection or expensive sensor-based systems, making them inefficient and costly. To address these challenges, we propose an IoT-based Pothole detection system utilizing an ESP8266 microcontroller, ultrasonic sensors, and a camera module. The system collects real-time road surface data, which is processed by a deep learning model techniques to detect Potholes accurately. The ESP8266 facilitates wireless data transmission to a central server for further analysis and mapping. This approach provides a cost-effective, automated, and scalable solution for road monitoring. By integrating to IoT and deep learning our system enhances Pothole detection 95% accuracy, reduces manual labour, and enables proactive road maintenance.

Source: Scientific Reports

DOI: 10.1038/s41598-026-62226-7

Authors: Raushan Kumar, Alok Priyadarshi, M. Shoba, Asisa Kumar Panigrahy, Vakkalakula Bharath Sreenivasulu

Institutions: SR University, National Institute of Technology Jamshedpur, ICFAI Foundation for Higher Education, Manipal Academy of Higher Education

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,690