Abstract
Damage detection via drones is fundamental in infrastructure health assessment. However, object scale variation due to drones' swift movement and sparse scenes make damage detection challenging. This paper describes a multi-task framework, EnsembleDetNet, for damage detection and multi-label scene classification by leveraging object detectors and classifiers based on ensemble learning which induces diversity and strength-correlation. Further, a novel attention module that significantly improves EnsembleDetNet by about 5% is proposed via explicit ensembling of parallel and sequential channel and spatial attention maps. Extensive experiments with a public dataset and an onsite verification utilizing a micro drone indicate that EsembleDetNet outperforms state-of-the-art detectors and classifiers under variant evaluation metrics. EnsembleDetNet has the potential to become a new paradigm in infrastructure health assessment.
•EnsembleDetNet, a generic multi-task framework, is proposed.•A novel explicit attention module that significantly boosts performance is proposed.•Engineering practicability is demonstrated for structural health assessment.•EnsembleDetNet attains better performance in detection and multi-label scene tasks.
