CRAAC: Consistency Regularised Active Learning with Automatic Corrections for Real-Life Road Image Annotations

Abstract

In annotating real-life large, noisy and domain-specific images for digitising infrastructure, substantial human effort persists despite past advancements. This research provides practical and interpretable scores for human annotators, enabling flexible annotation strategies, improving automation and reducing the effort required to create and correct image labels. The authors present the CRAAC solution: Consistency Regularised Active learning and Automatic Corrections, which builds on Mask R-CNN with three additional modules: consistency regularisation, scoring modules for active learning and automatic corrections. Experiments on our pavement image dataset, recorded with a low silhouette score of 0.146 and qualitative annotation inconsistencies, reduce the human effort of mouse clicks by 5-11% and improve the quality metrics of mAP and AR by approx. 40% from the original Mask R-CNN. The automatic correction further reduces the performance variation.