High-resolution infrastructure defect detection dataset sourced by unmanned systems and validated with deep learning

Visual inspection of civil infrastructures has traditionally been a crucial yet labor-intensive task. In contrast, unmanned robots equipped with deep learning-based visual defect detection methods offer a more comprehensive and efficient solution compared to conventional manual inspection techniques. However, the full potential of deep learning in defect detection has yet to be fully realized, primarily due to the scarcity of annotated, high-quality defect datasets. In this study, we introduce CUBIT-Det, a high-resolution defect detection dataset that includes over 5500 images captured under various scenarios using professional-grade equipment. Distinguishing itself from existing datasets, CUBIT-Det encompasses a wide array of practical situations, backgrounds, and defect categories. We perform extensive benchmarking experiments on the dataset with nearly 30 cutting-edge real-time detection methods, and analyze both the impact of the dataset's annotation methods and zero-shot transfer ability of it. This effort lays a robust foundation for future advancements in defect detection solutions. Additionally, the practicality and effectiveness of CUBIT-Det are confirmed through thorough inspections of real-world buildings. Finally, we detail the features and acknowledge the limitations of our dataset, thereby highlighting significant opportunities for future research. • Proposed and open-sourced a high-resolution infrastructure defect dataset: CUBIT-Det and analyzed the features of this dataset. • Evaluated the CUBIT-Det dataset with nearly 30 state-of-the-art deep learning-based real-time object detection models and conducted the comprehensive analysis. • Implemented the real-world experiment to verify the reliability and feasibility of CUBIT-Det dataset.