Investigation into autolabeling a novel dataset for off-road autonomy using transfer learning

In autonomous driving, utilizing deep learning models to help make decisions has become a popular theme, particularly in the realm of computer vision. These models are heavily geared to make decisions based on the environment in which they are trained. Currently, there are very few datasets that exist for off-road autonomy. In order for autonomous vehicles to traverse off-road or unstructured settings, the vehicles must have an understanding of these environments. This paper seeks to lay the groundwork for a new, off-road, multi-modality dataset, with initial data being collected at Mississippi State University’s Center for Advanced Vehicular Systems (MSU CAVS). This dataset will include co-aligned and coregistered LiDAR, thermal, and visual sensor data. Additionally, this dataset will include semantic segmented visual data as well as object detection and classification labels for all three modalities. However, to perform semantic segmentation on each image individually would be strenuous and time-consuming due to the large quantity of images. Thus, this paper will explore the utility of transfer learning for auto-labeling. Specifically, this paper considers how transfer learning for a unique and under-represented (data-wise) domain performs in reducing the burden associated with hand-annotation datasets for deep learning applications.