WebLabel: OpenLABEL-compliant multi-sensor labelling

Abstract

Annotated datasets have become crucial for training Machine Learning (ML) models for developing Autonomous Vehicles (AVs) and their functions. Generating these data- sets usually involves a complex coordination of automation and manual effort. Moreover, most available labelling tools focus on specific media types (e.g., images or video). Con- sequently, they cannot perform complex labelling tasks for multi-sensor setups. Recently, ASAM published OpenLABEL, a standard designed to specify an annotation format flex- ible enough to support the development of automated driving features and to guarantee interoperability among different systems and providers. In this work, we present WebLa- bel, the first multipurpose web application tool for labelling complex multi-sensor data that is fully compliant with OpenLABEL 1.0. The proposed work analyses several labelling use cases demonstrating the standard’s benefits and the application’s flexibility to cover various heterogeneous requirements: image labelling, multi-view video object annotation, point- cloud view-based labelling for 3D geometries and action recognition.