Autonomous health assessment of civil infrastructure using deep learning and smart devices

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 stateof-the-art detectors and classifiers under variant evaluation metrics. EnsembleDetNet has the potential to become a new paradigm in infrastructure health assessment.