Automated vision-based structural health inspection and assessment for post-construction civil infrastructure

Abstract

In the search to enhance the effectiveness and efficiency of structural health monitoring for preserving safety and integrity in civil infrastructure, the search for innovative technologies is of critical importance. This paper introduces ExpoDet, a comprehensive framework designed for autonomous health inspection and infrastructure assessment. ExpoDet features a multi-detection detector, autonomous navigation for micro aerial vehicles facilitated through secondary reward reinforcement learning, and a damage aggregation scheme for autonomous health assessment following detections. Moreover, it presents an attention module called EEAM+ that introduces dynamic feature orientation and significantly enhances the capabilities of ExpoDet. ExpoDet is extensively tested and evaluated in both offsite and field test experiments. Comparisons with several state-of-the-art object detectors coupled with attention modules show an average improvement of approximately 3% across various evaluation metrics. • ExpoDet, a multi-task framework, is proposed for autonomous structural health inspection and assessment. • Secondary reward reinforcement learning, which constrains two related robotic tasks, is proposed. • Dynamic orientation of feature maps is introduced into the explicit ensemble attention module. • ExpoDet demonstrates engineering feasibility and practicability with satisfactory performances.