The purpose of this document is to show evidence of the work carried out as part of the pre-flight flight validation activities of one RNAV approach Instrument Flight Procedures (IFP), down to LPV minima, at Katowice Airport (EPKT). The document is a deliverable of the TEN-T funded project "Support to the EGNOS APV Operational Implementation -APV MIELEC".
The purpose of this document is to present evidence of the work carried out as part of the flight validation activities of the RNAV approach involving the instrument flight procedures (IFPs), down to the localizer performance with vertical (LPV) minima, for RWY27 at Katowice Airport (EPKT). The presented material constitutes the second part of the "Preflight validation RNAV GNSS approach procedures for EPKT in the EGNOS APV Mielec" project. The following issues were addressed: flight validation conditions, list of performed approaches, flight path analysis and pilot feedback.
The paper focuses on the intelligent fault diagnosis system for automatic analysis of a huge amount of images with 4K resolution collected during flight inspection of the overhead power lines with the use of aerial platforms. The developed system can be used to detect and locate anomalies of power line structures e.g. anomalies of towers, conductors, dampers, insulators, etc. The purpose of the paper is to present the general architecture of the system and the results obtained during inspection of thousands of kilometer flights of power lines. The proposed system is based on the predefined deep neural network called Faster R-CNN which is dedicated for solving real-time object detection problems. The faster R-CNN-based neural model created on COCO dataset was additionally retrained by the authors applying images gathered as a result of flight inspections in order to obtain the high performance of the whole system. The comprehensive verification tests were carried out to prove the merits of the proposed solution.
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