Power infrastructure monitoring and damage detection using drone captured images
| Autor: | Jayavardhana Gubbi, Purushothaman Balamuralidhar, Hrishikesh Sharma, Ashley Varghese |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry 020209 energy Deep learning Real-time computing Feature extraction 02 engineering and technology Spectral clustering Drone Task (project management) Power (physics) Analytics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Aerial image |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2017.7966053 |
| Popis: | Infrastructure detection and monitoring is a difficult task. Due to the advances in unmanned vehicles and image analytics, it is possible to decrease the human effort and achieve consistent results in infrastructure assessments using aerial image processing. Reliable detection and integrity checking of power infrastructure including conductor lines, pylons and insulators in a diverse background is the most challenging task in drone based automatic infrastructure monitoring. Most techniques in literature use first principle approach that tries to represent the image as features of interest. This paper proposes a deep learning approach for power infrastructure detection. Graph based post processing is applied for improving the outcomes of the generated deep model. A f-score of 75% is achieved using the deep model which is further improved using spectral clustering for the conductor lines, pylons and insulators that form the core parts of power infrastructure. |
| Databáze: | OpenAIRE |
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