One-Vs-All Semi-Automatic Labeling Tool for Semantic Segmentation in Autonomous Driving

Semantic image segmentation plays a pivotal role in creating High-Definition (HD) maps for autonomous driving, where every pixel in an image is assigned a label from a specific semantic class. However, obtaining dense pixel-level annotations for model training is a laborious and expensive process. Active learning holds promise as a method to reduce the human annotation effort needed for semantic segmentation. However, existing active learning methods often perform well in the majority classes but struggle with the minority classes, negatively impacting segmentation performance. To tackle this challenge, we propose a novel One-vs-All (OVA) active learn- ing framework, known as OVAAL. This paper explains how OVAAL can shift more attention towards the minority classes and thoroughly analyzes its contributions to performance en- hancement. Additionally, we introduce an OVA-based semi- supervised learning method as the final training phase, referred to as OVAAL+. Our results demonstrate that both OVAAL and OVAAL+ lead to significant improvements, with mean Intersection over Union (mIoU) gains of 4.55% and 6.38%, respectively, compared to the state-of-the-art active learning method Pixelpick on the Cityscapes semantic segmentation benchmark. These improvements are achieved while maintain- ing an economical annotation budget of 1.44% of the training data. We foresee further research exploring the potential of OVA-based active selection to address challenges in cold start scenarios and resource-constrained training environments.