Jalil Bushra, Leone Giuseppe Riccardo, Martinelli Massimo, Moroni Davide, Pascali Maria Antonietta, Berton Andrea
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" CNR, 56124 Pisa, Italy.
Istituto di Fisiologia Clinica CNR, 56124 Pisa, Italy.
Sensors (Basel). 2019 Jul 9;19(13):3014. doi: 10.3390/s19133014.
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.
输电线路是发电厂与通过变电站的用电点之间的连接纽带。最重要的是，对受损架空电力线路和生锈导体的评估对公共安全至关重要；因此，必须定期检查电力线路及相关部件，以确保持续供电，并识别任何故障和缺陷。为实现这些目标，近年来，无人机得到了广泛应用；事实上，它们提供了一种安全的方式，可在不中断设备运行的情况下，将传感器靠近输电线路及其相关部件，同时降低运营成本和风险。在这项工作中，一架配备多模态传感器的无人机在可见光和红外领域捕捉图像，并将其传输到地面站。我们使用了最先进的计算机视觉方法来突出预期故障（即热点）或电气基础设施的受损部件（即受损绝缘子）。红外成像对实际运行环境中的大规模和光照变化具有不变性，有助于识别输电线路中的故障；同时，采用神经网络并对其进行训练，以从光学视频流中检测和分类绝缘子。我们在意大利帕尔马由无人机捕获的数据上展示了我们的方法。
