Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data

被引:77
作者
Jalil, Bushra [1 ]
Leone, Giuseppe Riccardo [1 ]
Martinelli, Massimo [1 ]
Moroni, Davide [1 ]
Pascali, Maria Antonietta [1 ]
Berton, Andrea [2 ]
机构
[1] CNR, Ist Sci & Tecnol Informaz Alessandro Faedo, I-56124 Pisa, Italy
[2] CNR, Ist Fisiol Clin, I-56124 Pisa, Italy
关键词
image analysis; RGB images; infrared images; wire detection; unmanned aerial vehicles; object detection; neural networks; RECOGNITION;
D O I
10.3390/s19133014
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.
引用
收藏
页数:15
相关论文
共 32 条
  • [1] Abadi M., 2015, P 12 USENIX S OPERAT
  • [2] Asiegbu G.O., 2013, LECT NOTES ELECT ENG, V221
  • [3] BONOTTO DM, 2010, WATER RESOUR PLAN DE, P1
  • [4] Detection of Thin Lines using Low-Quality Video from Low-Altitude Aircraft in Urban Settings
    Candamo, Joshua
    Kasturi, Rangachar
    Goldgof, Dmitry
    Sarkar, Sudeep
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2009, 45 (03) : 937 - 949
  • [6] Chen T, 2016, ADVANCES IN ENGINEERING MATERIALS AND APPLIED MECHANICS, P3
  • [7] Giones F, 2013, TECHNOL INNOV MANAG, P13
  • [8] Hines G., 2013, P SOC PHOTO-OPT INS, V5108, P231
  • [9] Hough P.V.C., 1962, U.S. Patent, Patent No. [3,069,654, 306965418]
  • [10] Jadin M.S., 2011, P PROGR EL RES S P M