Journal
REMOTE SENSING
Volume 11, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/rs11111342
Keywords
power line detection; convolutional neural networks; structured features; datasets
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61771351]
- CETC key laboratory of aerospace information applications [SXX18629T022]
- project for innovative research groups of the natural science foundation of Hubei Province [2018CFA006]
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Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods.
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