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• W2997220185 abstract "Galvanised steel transmission towers in electrical power grids suffer from corrosion to different levels depending on age and environment. To ensure the power grid can operate safely, significant resources are spent to monitor the corrosion level of towers. Photographs from helicopters, drones, and a variety of staffs are often used to capture condition, however, these images still need manual inspection to determine the corrosion level before carrying out maintenance works. In this paper, we describe a framework employing multiple deep neural networks based classifiers and detectors to perform automatic image-based condition monitoring for steel transmission towers. Given a random variety of images of a tower, our proposed framework will first determine the location of the image on the structure via a trained zone classifier. Then, fine-grain corrosion inspection will be performed on both fasteners and structural members, respectively. In addition, an automatic zoomin functionality will be applied to images which have high resolution but are a long distance away. This step will ensure the detection performance on small objects on the tower. Finally, the overall corrosion status report for this tower will be calculated and generated automatically. Additionally, we released a subset of our data to contribute to this novel direction. Experiments show that our framework can assess the tower efficiently." @default.
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• W2997220185 date "2019-12-01" @default.
• W2997220185 modified "2023-09-26" @default.
• W2997220185 title "Deep Corrosion Assessment for Electrical Transmission Towers" @default.
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• W2997220185 doi "https://doi.org/10.1109/dicta47822.2019.8945905" @default.
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