TerrainSense: Dataset of Off-Road Terrain Obstacles and Traversability Hazards

TerrainSense is a dataset of off-road object detections to help guide vehicles across terrains. There are 2,882 total labeled images, consisting of 11,841 bounding boxes, broken into train, validation and test sets. There are no images with missing labels. Data was collected in actual landscapes using standard consumer devices, then all images were labeled using a mix of human intervention and an iterative approach utilizing a YOLO model. After generating pseudo-labels, low-confidence bounding box imageswere flagged for additional review by humans and then reviewed in labelImg by people to validate the bounding boxes. Please refer to README.md for additional details about the overall dataset and for the corresponding paper draft at docs/terrainSense_research_paper.md. The TerrainSense dataset is organized into 4 classes: obstacle, person, pothole, vehicle. All of the classes are imbalanced (meaning class sizes do not match). The majority of the annotations are labeled as obstacles. The current statistics summary (dataset_stats.json) states that there are 2,303 images in the train set, 288 in the validation set, and 291 in the test set. The images vary in size from approximately 640×640 pixels to 1080×1920 pixels, making the image set contain images at multiple aspect ratios instead of a common fixed size.