Automatic Image Annotation for Mapped Features Detection
Maxime Noizet (UTC, Heudiasyc), Philippe Xu (ENSTA Paris), Philippe, Bonnifait (UTC, Heudiasyc)

TL;DR
This paper presents a multi-modal automatic annotation approach combining vector maps, lidar, and image segmentation to improve pole detection in road environments, reducing manual effort and enhancing perception models for autonomous driving.
Contribution
It introduces a novel fusion of three automatic annotation methods and demonstrates their effectiveness in improving pole detection accuracy and model training.
Findings
Multi-modal annotation fusion improves pole detection accuracy.
Enhanced annotations lead to better perception model training.
The approach reduces manual annotation efforts significantly.
Abstract
Detecting road features is a key enabler for autonomous driving and localization. For instance, a reliable detection of poles which are widespread in road environments can improve localization. Modern deep learning-based perception systems need a significant amount of annotated data. Automatic annotation avoids time-consuming and costly manual annotation. Because automatic methods are prone to errors, managing annotation uncertainty is crucial to ensure a proper learning process. Fusing multiple annotation sources on the same dataset can be an efficient way to reduce the errors. This not only improves the quality of annotations, but also improves the learning of perception models. In this paper, we consider the fusion of three automatic annotation methods in images: feature projection from a high accuracy vector map combined with a lidar, image segmentation and lidar segmentation. Our…
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Taxonomy
MethodsBalanced Selection
