DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets
This addresses the annotation bottleneck for autonomous driving datasets, offering an incremental improvement in efficiency for researchers and practitioners in computer vision.
The paper tackles the problem of generating precise ground truth bounding boxes for autonomous driving datasets, which is time-consuming and expensive, by proposing DeepBbox to correct loose labels, resulting in a 50% increase in automatically labeled object edges within 1% error to reduce manual annotation time.
Autonomous driving requires various computer vision algorithms, such as object detection and tracking.Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that corrects loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox,we can increase the number of object edges that are labeled automatically (within 1\% error) by 50% to reduce manual annotation time.