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.

翻译：在结构损伤检测中，目标检测器的准确检测与鲁棒性对于保障民用基础设施的持续使用至关重要。然而，实现目标检测器的鲁棒性仍是一个持续存在的挑战，这影响了其有效泛化的能力。本研究提出了DetectorX，一个结合微型无人机的鲁棒结构损伤检测框架。DetectorX通过引入两个创新模块——茎块模块和螺旋池化技术，应对目标检测器鲁棒性面临的挑战。茎块模块通过利用两个深度卷积神经网络模型的输出，引入了一种动态视觉模态。该框架采用所提出的基于事件的奖励强化学习来约束父、子DCNN模型的行为，从而产生奖励。这导致在红、绿、蓝数据之外，诱导出两种动态视觉模态。这一增强显著提升了DetectorX在不同环境情境下的感知与适应能力。此外，作为一种在线图像增强方法的螺旋池化技术，通过拼接螺旋池化特征与平均/最大池化特征来增加特征表示，从而强化了该框架。在三个广泛的实验中：(1) 对比实验和(2) 鲁棒性实验（使用太平洋地震工程研究中心ImageNet数据集），以及(3) 现场实验，DetectorX在各项指标上均表现良好，包括精确率（0.88）、召回率（0.84）、平均精确率（0.91）、平均精确率均值（0.76）和平均召回率均值（0.73），优于包括YOLOX-m在内的其他竞争检测器。研究结果表明，DetectorX能够提供令人满意的结果，并在具有挑战性的环境中展现出良好的鲁棒性。