Payload Assisted Unmanned Aerial Vehicle Structural Health Monitoring (UAVSHM) for Active Damage Detection

Abstract

Structural Health Monitoring (SHM) incorporates techniques for implementing a damage detection and characterization for engineering structures. Visual inspection remains the primary technique to inspect the health of civil structures such as bridges, roads, dams and buildings despite being time-consuming and posing a significant risk to human life in inspecting specific structures. Unmanned Aerial Vehicle Assisted Structural Health Monitoring (UAVSHM) has emerged as a viable and promising option for SHM. However, UAVs are equipped with limited battery supply and onboard computational power. They are unsuitable for active damage detection, i.e., detecting damages to civil structures in real time. This paper firstly advocates using an edge device as a UAV payload to conduct active damage detection. Secondly, it compares traditional machine learning and transfer learning approaches to build Convolution Neural Network (CNN) models for damage detection using an edge device. The results show that edge devices have an acceptable latency to facilitate real-time damage detection for UAVSHM.