Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone (1801.09454v2)

Abstract: Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).

• The paper presents a comprehensive dataset of 9,053 images with 15,435 annotated instances, establishing a benchmark for road damage detection research.
• The paper employs CNN-based object detection models that classify damage into eight categories with precision and recall rates exceeding 75% for key types.
• The paper demonstrates a practical smartphone application using SSD MobileNet, enabling real-time detection of road damage in approximately 1.5 seconds.

Road Damage Detection Using Deep Neural Networks

The paper, "Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone," addresses the challenges inherent in assessing and maintaining road infrastructure by leveraging deep learning techniques. The paper primarily seeks to bridge the gap between existing research limitations and practical implementation needs for road damage detection through a systematic and data-driven approach.

Key Contributions

1. Creation of a Comprehensive Dataset: As a foundational effort, the researchers have compiled and released a large-scale dataset of road damage images—a first of its kind. This dataset consists of 9,053 images with 15,435 annotated instances of road damage captured over 40 hours across varying weather and illumination conditions. This initiative introduces a benchmark dataset crucial for advancing and standardizing road damage detection research.
2. Implementation of Deep Learning Techniques: The paper utilizes state-of-the-art object detection models, particularly convolutional neural networks (CNNs), to classify road damage into eight distinct categories. The models were trained and evaluated using the specially curated dataset, with strong performance indicators for key categories.
3. Practical Application and Accessibility: The research includes the development of a smartphone application capable of processing images in real-time to detect road damage with notable accuracy. The app leverages models like SSD MobileNet, achieving detection speeds of 1.5 seconds on smartphones. This positions the paper's outcomes as directly applicable to real-world scenarios where efficient and accessible infrastructure inspection methods are required.

Numerical Results and Evaluation

The paper presents quantitative results for model performance across different categories of road damage. The models achieve recall and precision percentages surpassing 75% for the most identifiable damage types, underscoring their efficacy. This level of accuracy is promising for practical deployment, enabling municipalities to efficiently identify and address road infrastructure issues.

Implications and Future Directions

From a practical standpoint, this paper provides a scalable solution to municipalities struggling with resource constraints for infrastructure maintenance. The deployment of smartphone-based detection tools can democratize access to advanced road inspection technologies, potentially becoming a standard component in urban infrastructure management.

Theoretically, this work stimulates further research into improving model accuracy, particularly for rare and less represented damage types in the dataset. Future research could explore training techniques to optimize detection performance in such underrepresented categories, as well as expanding the dataset to enhance variability and robustness.

Conclusion

This paper presents a structured approach to road damage detection using deep learning, backed by a comprehensive dataset and a practical implementation framework. The alignment of theoretical advances with real-world applications positions this paper as a significant contribution towards automating and improving infrastructure maintenance practices. Future research will likely extend these methodologies to broader contexts and explore further enhancements in detection technologies.