Enhancing Real Time Pothole Detection Using Vehicles on Machine Learning

Abstract

Machinelearning(ML)isabranchofcomputerscienceandAI,thathelpsmachinelearningfrom data and perform some tasks that humans can do it train machines to perform task like analyze data categorizing images, etc. In the existing system, they have used YOLOv2 algorithm to detect and identify potholes. They proposed a automated system that determines the state of the road. They trained the system by using images captured from mobile cameras will be tested on several road images.Even though the existing work is effective, there are some issues such as it does not provide accuracywithdetectingsmallobjectsinimagesandalsotheboxprecisioncanbelessaccurate.It requires high resources for Real-time applications with-High Resolution images. To address the issues, We proposed a YOLOv8 advanced algorithm. Which offer better accuracy and precision inobjectdetection.YOLOv8enhancesspeedandefficiencyinRealtimedetectiontechniqueand alsoitisfasterindetectingtheHighResolutionimages.Soourproposedworkwillovercomethese challenges by providing better Efficiency and Accuracy

Introduction

Problem:
Potholes are a serious issue on roads, particularly in countries like India, causing accidents and vehicle damage. Traditional detection methods—manual inspections and sensor-based approaches—are labor-intensive, costly, and inefficient.

Objective:
To develop a real-time, automated pothole detection system using the YOLO algorithm and OpenCV that eliminates manual labor, reduces costs, and improves road safety.

Proposed Solution:

• The system uses road images (720×720 resolution) processed with YOLO (You Only Look Once) and OpenCV for accurate, real-time pothole detection.

• It provides immediate alerts and location reports, helping authorities prioritize repairs.

• Future enhancements could include GPS-based tagging and severity analysis.

Smart City Integration:
This system supports smart city goals by contributing to comprehensive road condition databases for better infrastructure planning.

Related Work:

Several studies have explored road crack and pothole detection using computer vision and machine learning:

• Panop Khumsap et al.: Used profiling techniques with SVM and Decision Tree classifiers.

• Lan Uong et al.: Developed a YOLOv2-based distress analyzer for road anomalies.

• Shan Luo et al.: Applied machine vision and image processing for crack classification and measurement.

• Zhang Yuhan et al.: Used embedded cameras with FAST feature point recognition for low-cost crack detection.

• Munish Bhardwaj et al.: Proposed a new segmentation technique (MHFCM) for accurate detection of various crack types.

System Overview:

The system turns vehicles into mobile sensing units using:

• Accelerometer & Gyroscope: Detect vertical vibrations and differentiate potholes from other surface irregularities.

• GPS Module: Tags the pothole’s location.

Advantages:

• Automated and real-time detection

• Scalable and cost-effective

• Enhanced road safety

• Data-driven infrastructure maintenance

Design & Implementation:

1. Sensor Setup: Vehicles are equipped with accelerometers, gyroscopes, and GPS modules.

2. Data Processing: Collected data is filtered and normalized using a Raspberry Pi or microcontroller.

3. Machine Learning Model: Uses SVM, Random Forest, or CNN to classify potholes based on extracted features.

4. Cloud Integration: GPS coordinates and severity scores are sent to a cloud server, where data from multiple vehicles is validated and mapped.

5. Dashboard: Road authorities access a centralized map for efficient pothole management.

Conclusion

We have successfully laid a strong foundationforourproposedwork,“realtime pothole detection using vehicles in machine learning. we studied a detailed overview of theexisting surveypaper andunderstandthe existing road damage detection techniques andidentifiedthecriticalchallenges,suchas high cost implementation and limitations in real time accuracy on existing road damage detectionwe also completedthesetup ofour development environment ,installing the necessarytoolsandconfiguringsoftwarelike python and flask to support our model development. The scope of this project focuses on enhancing road safety and infrastructuremanagementthroughreal-time potholedetectionusingtheYOLOalgorithm and OpenCV. The system is designed to identifypotholesinlivecamerafeedsorrecorded videos with high accuracy, making it suitable for diverse environmental conditions. It aims to assist municipal and government agencies by automating pothole detection, reducing dependency on manual inspections, and saving time and costs.

References

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Copyright

Copyright © 2025 Mrs. R. Jayalakshmi, Dr. R. G. Suresh Kumar, V. Javitha, R. Liana Lequatorze, R. Dhivyapriya, S. Swetha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.