Abstract
Accurate detection and resilience of object detectors in structural damage
detection are important in ensuring the continuous use of civil infrastructure.
However, achieving robustness in object detectors remains a persistent
challenge, impacting their ability to generalize effectively. This study
proposes DetectorX, a robust framework for structural damage detection coupled
with a micro drone. DetectorX addresses the challenges of object detector
robustness by incorporating two innovative modules: a stem block and a spiral
pooling technique. The stem block introduces a dynamic visual modality by
leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models.
The framework employs the proposed event-based reward reinforcement learning to
constrain the actions of a parent and child DCNN model leading to a reward.
This results in the induction of two dynamic visual modalities alongside the
Red, Green, and Blue (RGB) data. This enhancement significantly augments
DetectorX's perception and adaptability in diverse environmental situations.
Further, a spiral pooling technique, an online image augmentation method,
strengthens the framework by increasing feature representations by
concatenating spiraled and average/max pooled features. In three extensive
experiments: (1) comparative and (2) robustness, which use the Pacific
Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment,
DetectorX performed satisfactorily across varying metrics, including precision
(0.88), recall (0.84), average precision (0.91), mean average precision (0.76),
and mean average recall (0.73), compared to the competing detectors including
You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate
that DetectorX can provide satisfactory results and demonstrate resilience in
challenging environments.