Phenotyping of Pine Tree Architecture with Stereo Vision and Deep Learning

2021 ◽  
Author(s):  
Mousumi Akter ◽  
Nariman Niknejad ◽  
Yin Bao ◽  
Rafael Bidese Puhl ◽  
Kitt Payn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stereo Vision ◽  
Tree Architecture ◽  
Pine Tree

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

2019 ◽  
Author(s):  
Alex Bertino ◽  
Mostafa Bagheri ◽  
Miroslav Krstić ◽  
Peiman Naseradinmousavi
Keyword(s):  
Deep Learning ◽  
Stereo Vision ◽  
Robot Manipulator ◽  
Control Techniques ◽  
Minimal Error ◽  
Autonomous Operation ◽  
Pick And Place ◽  
Place Task ◽  
Position And Orientation ◽  
And Control

Abstract In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.

In-situ concrete slump test incorporating deep learning and stereo vision

2021 ◽  
Vol 121 ◽  
pp. 103432
Author(s):  
Nguyen Manh Tuan ◽  
Quach Van Hau ◽  
Sangyoon Chin ◽  
Seunghee Park
Keyword(s):  
Deep Learning ◽  
Stereo Vision ◽  
Slump Test

scholarly journals Review of Stereo Matching Algorithms Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Kun Zhou ◽  
Xiangxi Meng ◽  
Bo Cheng
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Learning Algorithms ◽  
Time Consumption ◽  
End To End ◽  
Traditional Approaches ◽  
Speed Accuracy ◽  
Comprehensive Coverage

Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches. This review presents an overview of different stereo matching algorithms based on deep learning. For convenience, we classified the algorithms into three categories: (1) non-end-to-end learning algorithms, (2) end-to-end learning algorithms, and (3) unsupervised learning algorithms. We have provided a comprehensive coverage of the remarkable approaches in each category and summarized the strengths, weaknesses, and major challenges, respectively. The speed, accuracy, and time consumption were adopted to compare the different algorithms.

Deep learning-based dead pine tree detection from unmanned aerial vehicle images

2020 ◽  
Vol 41 (21) ◽  
pp. 8238-8255
Author(s):  
Huan Tao ◽  
Cunjun Li ◽  
Dan Zhao ◽  
Shiqing Deng ◽  
Haitang Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Tree Detection ◽  
Pine Tree ◽  
Aerial Vehicle

Automated pixel-level pavement distress detection based on stereo vision and deep learning

2021 ◽  
Vol 129 ◽  
pp. 103788
Author(s):  
Jinchao Guan ◽  
Xu Yang ◽  
Ling Ding ◽  
Xiaoyun Cheng ◽  
Vincent C.S. Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stereo Vision ◽  
Pavement Distress ◽  
Pavement Distress Detection
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