scholarly journals Distributed sensing technology of high-spatial resolution based on dense ultra-short FBG array with large multiplexing capacity

2017 ◽  
Vol 25 (23) ◽  
pp. 28112 ◽  
Author(s):  
Gui Xin ◽  
Li Zhengying ◽  
Wang Fan ◽  
Wang Yiming ◽  
Wang Changjia ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Distributed Sensing ◽  
Sensing Technology

scholarly journals Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Jingxian Yu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Collapsed Buildings ◽  
Improved Model ◽  
Detection Speed

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

scholarly journals High spatial resolution distributed sensing in optical fibers by Brillouin gain-profile tracing

2010 ◽  
Vol 18 (8) ◽  
pp. 8671 ◽  
Author(s):  
Tom Sperber ◽  
Avishay Eyal ◽  
Moshe Tur ◽  
Luc Thévenaz
Keyword(s):  
Spatial Resolution ◽  
Optical Fibers ◽  
High Spatial Resolution ◽  
Distributed Sensing ◽  
Gain Profile ◽  
Brillouin Gain

scholarly journals Investigation of a Signal Demodulation Method based on Wavelet Transformation for OFDR to Enhance Its Distributed Sensing Performance

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2850 ◽  
Author(s):  
Kunpeng Feng ◽  
Jiwen Cui ◽  
Hong Dang ◽  
Xun Sun ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Wavelet Transformation ◽  
High Spatial Resolution ◽  
Morlet Wavelet ◽  
Distributed Sensing ◽  
Spatial Interval ◽  
Signal Demodulation ◽  
Short Time ◽  
Demodulation Method ◽  
Short Time Fourier Transformation

Optical fiber distributed sensing that is based on optical frequency domain reflectometer (OFDR) is a promising technology for achieving a highest spatial resolution downwards to several millimeters. An OFDR signal demodulation method that is based on Morlet wavelet transformation (WT) is demonstrated in detail to improve the resolution of distributed sensing physical quantity under a high spatial resolution, aiming at the trade-off between spatial and spectrum resolution. The spectrum resolution, spatial interval of the measured gauges, and spatial resolution can be manually controlled by adjusting the wavelet parameters. The experimental results that were achieved by the wavelet transformation (WT) method are compared with these by short time Fourier transformation (STFT) method and they indicate that significant improvements, such as strain resolution of 1 με, spatial resolution of 5 mm, average repeatability of 4.3 με, and stability of 7.3 με within one hour, have been achieved. The advantages of this method are high spatial and spectral resolution, robust, and applicability with current OFDR systems.

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