{"title":"A UAV-Based Measurement System for Aircraft Skin Defect Detection Using a State-Space Model Approach","authors":"Mengyao Feng;Yuanming Xu;Wei Dai;Haibo Luo","doi":"10.1109/TIM.2025.3606070","DOIUrl":null,"url":null,"abstract":"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 <inline-formula> <tex-math>$640 \\times 640$ </tex-math></inline-formula> frame in 4.2 ms (<inline-formula> <tex-math>$\\approx 238$ </tex-math></inline-formula> 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.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151238/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.