Transfer Learning-based Road Damage Detection for Multiple Countries (2008.13101v1)

Abstract: Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones. Thirdly, we propose generalized models capable of detecting and classifying road damages in more than one country. Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. Our dataset is available at (https://github.com/sekilab/RoadDamageDetector/).

• The paper shows that a Japanese road damage detection model can be adapted for multiple countries using transfer learning.
• It leverages a heterogeneous dataset of 26,620 road images from Japan, India, and the Czech Republic to enhance model robustness.
• The study develops hybrid models that improve detection accuracy and offer practical recommendations for scalable infrastructure maintenance.

Transfer Learning-based Road Damage Detection Across Multiple Countries

The paper "Transfer Learning-based Road Damage Detection for Multiple Countries" addresses the challenges associated with automated road damage detection on a global scale. It focuses on overcoming the technological and financial barriers encountered by municipalities worldwide, particularly in developing countries, where high-cost equipment for road condition monitoring is often inaccessible. Utilizing transfer learning, the research evaluates the adaptability of a Japanese model for road damage detection in various international contexts.

Key Contributions

1. Cross-Country Applicability: The paper investigates whether a road damage detection model developed in Japan can be effectively applied in other countries. This involves assessing the model's performance across diverse road conditions and infrastructure characteristics.
2. Heterogeneous Dataset: A substantial contribution is the development of a diverse dataset comprising 26,620 road images from Japan, India, and the Czech Republic. This dataset facilitates the training of models on varied road conditions, thus enhancing their robustness.
3. Generalized Detection Models: The paper proposes hybrid models capable of detecting and classifying road damages across different countries. Such models aim to ensure efficient applicability beyond the boundaries of a single national dataset.
4. Practical Recommendations: The authors provide strategic advice for local governments and agencies to utilize and adapt existing models for road damage detection, promoting the global adoption of such technologies.

Methodology and Experiments

The methodology involves the use of Convolutional Neural Networks (CNNs), specifically focusing on mobile-friendly architectures like SSD MobileNet. The models are trained and evaluated using transfer learning across multiple datasets from the contributing countries.

Experimentation

• Single Source vs. Multiple Source Models: The experiments differentiate between models trained using data from a single country (e.g., Japan) and those using combined datasets from multiple countries. The latter approach often enhances generalizability by leveraging diverse road characteristics.
• Model Performance Across Targets: Each model's performance is assessed across various countries, revealing that models trained with mixed datasets generally perform better than those trained with data from a single source.

Implications and Analysis

The analysis discovers that incorporating data from multiple sources not only improves model accuracy but also prevents overfitting, leading to a more generalized solution. However, the performance improvements differ among countries due to variations in road characteristics and damage types. Notably, hybrid models improve detection for specific damage types and enhance model applicability across new regions.

Practical Implications and Future Directions

The research holds significant practical implications for municipalities seeking cost-effective and scalable solutions for road maintenance. By leveraging existing datasets and models from countries like Japan, other regions can develop efficient road damage detection systems without starting from scratch.

The paper advocates for further investigations into global standardization models that could unify road damage detection technologies. It suggests leveraging public participation and additional data sources to enrich dataset diversity and improve model robustness.

In summary, this paper demonstrates the potential for transfer learning in extending the applicability of automated road damage detection beyond technical and geographic boundaries, offering significant advantages for global infrastructure maintenance.