A UAV-Based Measurement System for Aircraft Skin Defect Detection Using a State-Space Model Approach

This article presents an unmanned aerial vehicle (UAV)-based vision measurement system for real-time aircraft skin defect detection. Existing small-target detection algorithms often rely on transformer architectures, which, despite their accuracy, suffer from high computational complexity. To address this, we propose a novel object detection approach based on a learned neural state-space model (SSM), where image features are represented as latent dynamic systems governed by recurrent state updates rather than attention mechanisms. Experimental results show performance gains over You Only Look Once (YOLO) v8, with improvements of 2.1%, 8.1%, and 1.7% in precision, recall, and mAP50, respectively. The proposed model processes a single $640 \times 640$ frame in 4.2 ms ( $\approx 238$ FPS) on an RTX 4090, demonstrating that while primarily improving detection accuracy, its linear-time complexity still ensures the real-time processing capability required for UAV-based inspection. Field tests on helicopters and transport aircraft confirm the system’s robustness, repeatability, and practical value for structural health monitoring. This work contributes a lightweight, efficient vision-based instrumentation solution incorporating dynamic modeling into the measurement process.