Work

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

Year: 2025

Type: conference-paper

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.

Source: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

DOI: 10.1109/wacv61041.2025.00468

Authors: Percy Lam, Sooyong Park, Weiwei Chen, Lavindra De Silva, Ioannis Brilakis

Institutions: University of Cambridge, University College London

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Awards: Digital Roads, EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Resilience in a Changing World (FIBE2)