scholarly journals Robust power line detection with particle-filter-based tracking in radar video

Author(s):  
Qirong Ma ◽  
Darren S. Goshi ◽  
Long Bui ◽  
Ming-Ting Sun

In this paper, we propose a tracking algorithm to detect power lines from millimeter-wave radar video. We propose a general framework of cascaded particle filters which can naturally capture the temporal correlation of the power line objects, and the power-line-specific feature is embedded into the conditional likelihood measurement process of the particle filter. Because of the fusion of multiple information sources, power line detection is more effective than the previous approach. Both the accuracy and the recall of power line detection are improved from around 68% to over 92%.

2011 ◽  
Vol 20 (12) ◽  
pp. 3534-3543 ◽  
Author(s):  
Qirong Ma ◽  
D. S. Goshi ◽  
Yi-Chi Shih ◽  
Ming-Ting Sun

2019 ◽  
Vol 11 (11) ◽  
pp. 1342 ◽  
Author(s):  
Heng Zhang ◽  
Wen Yang ◽  
Huai Yu ◽  
Haijian Zhang ◽  
Gui-Song Xia

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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