BDRoad-Sense: A Benchmark Dataset for Road Surface Detection and Safety Enhancement

The BDRoad-Sense dataset was developed to support research on automated road surface monitoring and road safety assessment in real-world environments. It is a multi-class image dataset that represents common road surface conditions and infrastructure components that can directly affect driving safety and transportation planning. The dataset includes five annotated categories: Major Damage, Minor Damage, Normal Road, Manhole, and Speed Breaker, allowing both damaged and functional road surface elements to be identified within a unified framework.

Road surface images were collected through field visits across various rural and urban locations in the Sylhet District of Bangladesh between November 2025 and February 2026. The images were captured using four different smartphone cameras under natural lighting and weather conditions without any artificial setup. This approach was followed to reflect real-world road inspection scenarios and to capture variations in illumination, pavement texture, camera resolution, and viewing angles typically encountered in mobile-based data acquisition systems.

In total, 6,350 road images were initially collected and manually screened to ensure visual clarity and class relevance. Only images in which the primary road surface feature was clearly visible were retained for further processing. Each image was then annotated according to predefined class definitions to maintain labeling consistency throughout the dataset. To address class imbalance and improve variability within individual categories, controlled data augmentation techniques such as brightness adjustment, contrast variation, and blur simulation were applied, resulting in an expanded dataset of 12,687 images while preserving the structural characteristics of each class.

The repository includes both the processed (augmented) and original data representations. Specifically, all images used in experiments are provided in a standardized format, resized to a uniform resolution of 1024 × 1024 pixels and stored in .JPG format. The processed dataset consists of augmented images generated through controlled transformations. The original dataset is provided in resized form (1024 × 1024 resolution) rather than in its initial raw capture resolution. Additionally, a metadata CSV file is included, containing structured information such as image path, class label, location, area type, and capturing device, enabling efficient data organization, filtering, and reproducibility of experiments.

The dataset is organized in a structured format suitable for supervised multi-class classification tasks and can be used to benchmark both convolutional and transformer-based models. By incorporating diverse road conditions from rural and urban transportation environments, BDRoad-Sense provides a practical resource for developing and evaluating automated road monitoring systems aimed at improving infrastructure maintenance and transportation safety.