Lightweight Yet High-Performance Defect Detector for Uav-Based Large-Scale Infrastructure Real-Time Inspection

Abstract

Defect diagnosis in urban infrastructure is crucial for public safety. Traditional manual inspections face significant challenges in terms of accuracy and cost-effectiveness. In this paper, we propose a lightweight and hardware-friendly large-scale infrastructure detector, CUPID, highly suitable for unmanned aerial vehicles (UAVs). Given the significant challenges in automatically detecting defects of varying intensity and size within complex infrastructure, along with the tendency of lightweight models to lose detail and fail to fully capture features during the defect extraction process, we propose the CUPID_Block, a multi-level information fusion block to construct the backbone, featuring the CUPID_Conv module equipped with our proposed CCA (CrissCross Attention). Furthermore, CUPID features an auxiliary training branch that assimilates lower feature maps, helping to recover details lost in deeper convolutional layers. To verify the effectiveness of CUPID and to address the lack of a suitable dataset in the community, we establish a multi-scenario infrastructure defect dataset, CUBIT2024, to conduct extensive experiments. Finally, to assess the efficiency and adaptability of CUPID in UAV for online infrastructure inspection, we design a compact autonomous drone, CU-Astro, where the proposed CUPID is deployed on the Jetson Orin NX computer onboard to evaluate the speed and power consumption of the inference.