Extending the Detector’s Label Space Using Pseudo Labeling | Hailiang Zhu
Extending the Detector’s Label Space Using Pseudo Labeling
Hailiang Zhu, RISS 2023 Cohort
Carnegie Mellon University
Robotics Institute Summer Scholars: https://riss.ri.cmu.edu/
The effectiveness of self-driving car technologies relies on accurately labeled datasets for training models. However, auto-vehicle companies face challenges when new uncategorized objects emerge, making the process of adding new labels time-consuming and expensive. We now possess a construction zone-specific dataset, Roadbotics, consisting of 29 manually labeled categories of construction zone-related objects. However, the dataset lacks crucial information about road structures, such as lane markings, which are essential for navigating through construction zones. This gap motivates us to extend the dataset with objects that are beneficial for navigation in construction zones. To automate the labeling process of construction zones, we propose a pipeline that utilizes advanced segmentation models to do pseudo-labeling. These models generate masks for object categories, which are then converted into COCO-compliant polygonal shapes for labeling. We train a new model with the augmented dataset to enhance its ability to accurately detect new categories. Beyond dataset extension, our research has broader implications.
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