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Updated: May 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Modified MobileNetV2 transfer learning model to detect road potholes.

Neha Tanwar¹, Anil V Turukmane¹

¹School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.

Peerj. Computer Science

|February 3, 2025

Summary

This study introduces a modified MobileNetV2 (MMNV2) model for accurate pothole detection in pavement images. The deep learning approach significantly improves infrastructure assessment and public safety through efficient road condition analysis.

Keywords:

Classification Deep learning Deep neural network MMNV2 Model Pavement Pothole detection Transfer learning

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Potholes and other road damages degrade pavement infrastructure, impacting safety and maintenance costs.
Accurate detection of pavement distress is essential for timely repairs and infrastructure management.
Current methods for road damage assessment can be labor-intensive and subjective.

Purpose of the Study:

To develop and evaluate deep learning (DL) models for enhanced pothole detection in pavement images.
To introduce a novel modified MobileNetV2 (MMNV2) model optimized for efficient feature extraction and accurate classification.
To compare the performance of MMNV2 against fourteen other established DL models for pavement assessment.

Main Methods:

Utilized transfer learning and deep learning techniques to preprocess digital pavement images.
Evaluated fourteen DL models including MobileNetV2, DenseNet, ResNet, and EfficientNet variants.
Developed a modified MobileNetV2 (MMNV2) model by integrating a five-layer pre-trained network for improved performance.
Trained and tested models on a dataset of 5,000 pavement images with a learning rate of 0.001.

Main Results:

The MMNV2 model achieved superior performance in classification, detection, and prediction accuracy compared to other evaluated models.
Achieved 99.95% accuracy in classifying images as 'normal' or 'pothole'.
Reported 100% recall, 99.90% precision, 99.95% F1-score, and a minimal 0.05% error rate.

Conclusions:

The MMNV2 model offers a highly accurate and efficient solution for automated pothole detection.
The model's reduced parameter count and superior performance make it suitable for real-world pavement assessment applications.
This deep learning approach enhances infrastructure maintenance strategies and contributes to public safety.