scholarly journals Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775282 ◽  
Author(s):  
Xiaolong Hui ◽  
Jiang Bian ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Transmission Tower ◽  
Correlation Filters ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Perspective Image ◽  
Aerial Vehicle

This article presents an autonomous navigation approach based on a transmission tower for unmanned aerial vehicle (UAV) power line inspection. For this complex vision task, a perspective navigation model, which plays an important role in the description and analysis of the flight strategy, is introduced. Based on the proposed navigation model, valuable cues are excavated from a perspective image, which enhances the capability of the perception of three-dimensional direction and simultaneously improves the safety of intelligent inspection. Specifically, for robust and continuous localization of the transmission tower, a developed detecting-tracking visual strategy—comprised tower detection based on a faster region-based convolutional neural network and tower tracking by kernelized correlation filters—is presented. Further, segmentation by fully convolutional networks is applied to the extraction of transmission lines, from which the vanishing point (VP), an important basis for determining the flight heading, can be obtained. For more robust navigation, the designed scheme addresses the scenario of a nonexistent VP. Finally, the proposed navigation approach and constructed UAV platform were evaluated in a practical environment and achieved satisfactory results. To the best of our knowledge, this article marks the first time that a navigation approach based on a transmission tower is proposed and implemented.

scholarly journals Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1099 ◽  
Author(s):  
Ahram Song ◽  
Yongil Kim
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
Ground Truth ◽  
Training Data ◽  
Fine Tuning ◽  
Target Domain ◽  
Ground Truth Data ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Aerial Vehicle

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

scholarly journals A monocular-based navigation approach for unmanned aerial vehicle safe and autonomous transmission-line inspection

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982994 ◽  
Author(s):  
Xiaolong Hui ◽  
Jiang Bian ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Spatial Distance ◽  
Vanishing Point ◽  
Transmission Tower ◽  
Flight Direction ◽  
Field Environment ◽  
Smooth Tracking ◽  
Aerial Vehicle

This article presents a monocular-based navigation approach for unmanned aerial vehicle safe and continuous inspection along one side of transmission lines. To this end, a navigation model based on the transmission tower and the transmission-line vanishing point was proposed, and the following three key issues were addressed. First, a deep-learning-based object detection and a fast and smooth tracking algorithm based on the kernelized correlation filter were combined to locate transmission tower timely and reliably. Second, the vanishing point of transmission lines was computed and optimized to provide unmanned aerial vehicle with a robust and precise flight direction. Third, to keep a stable safe distance from transmission lines, the transmission lines were first rectified by optimizing a homography matrix to eliminate the parallel distortion, and then their interval variation was estimated for reflecting the spatial distance variation. Finally, the real distance from transmission tower was measured by the triangulation across multiple views. The proposed navigation approach and the designed UAV platform were tested in a field environment, which achieved an encouraging result. To the best of authors’ knowledge, this article marks the first time that a safe and continuous navigation approach along one side of transmission lines is put forward and implemented.

scholarly journals Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3106 ◽  
Author(s):  
Chengquan Zhou ◽  
Hongbao Ye ◽  
Jun Hu ◽  
Xiaoyan Shi ◽  
Shan Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
Disease Diagnosis ◽  
Global Scale ◽  
High Definition ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection And Counting ◽  
Aerial Vehicle

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

Unmanned Aerial Vehicle for Road Monitoring: Fully Convolutional Networks Approach

2019 ◽  
Vol 12 (6) ◽  
Author(s):  
Laksh kotian ◽  
Asita chheda ◽  
Vaibhav Narwane ◽  
Rakesh Raut
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Aerial Vehicle

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110381
Author(s):  
Xue Bai ◽  
Ze Liu ◽  
Jie Zhang ◽  
Shengye Wang ◽  
Qing Hou ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Of Interest ◽  
Dice Similarity Coefficient ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Dose Distributions ◽  
Convolutional Networks ◽  
Dose Prediction ◽  
Fully Convolutional Networks ◽  
Percentage Difference

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

Power Transmission Lines Inspection using Properly Equipped Unmanned Aerial Vehicle (UAV)

2018 ◽  
Author(s):  
Alexandros Zormpas ◽  
Konstantia Moirogiorgou ◽  
Kostas Kalaitzakis ◽  
George A. Plokamakis ◽  
Panayotis Partsinevelos ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Aerial Vehicle

scholarly journals PRELIMINARY EVALUATION OF ATMOSPHERIC TEMPERATURE AND WIND PROFILES OBTAINED USING UNMANNED AERIAL VEHICLE BASED ACOUSTIC TOMOGRAPHY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 283-287 ◽  
Author(s):  
A. Finn ◽  
K. Rogers ◽  
J. Meade ◽  
J. Skinner ◽  
A. Zargarian
Keyword(s):  
Wind Velocity ◽  
Unmanned Aerial Vehicle ◽  
Sound Speed ◽  
Three Dimensional ◽  
Atmospheric Temperature ◽  
Preliminary Evaluation ◽  
Sound Fields ◽  
Aerial Vehicle ◽  
Wind Profiles ◽  
Virtual Temperature

<p><strong>Abstract.</strong> An acoustic signature generated by an unmanned aerial vehicle is used in conjunction with tomography to remotely sense temperature and wind profiles within a volume of atmosphere up to an altitude of 120&amp;thinsp;m and over an area of 300&amp;thinsp;m&amp;thinsp;&amp;times;&amp;thinsp;300&amp;thinsp;m. Sound fields recorded onboard the aircraft and by an array of microphones on the ground are compared and converted to sound speed estimates for the ray paths intersecting the intervening medium. Tomographic inversion is then used to transform these sound speed values into three-dimensional profiles of virtual temperature and wind velocity, which enables the atmosphere to be visualised and monitored over time. The wind and temperature estimates obtained using this method are compared to independent measurements taken by a co-located mid-range ZephIR LIDAR and sensors onboard the aircraft. These comparisons show correspondences to better than 0.5&amp;thinsp;&amp;deg;C and 0.3&amp;thinsp;m/s for temperature and wind velocity, respectively.</p>

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