Delft University of Technology
Fast Symmetry Detection with Deep Learning and GeConv
Mkhoyan, T.
Publication date 2019
Document Version Final published version Citation (APA)
Mkhoyan, T. (2019). Fast Symmetry Detection with Deep Learning and GeConv. Poster session presented at IEEE RAS 2019 International Summer School on Deep Learning for Robot Vision, Santiago, Chile. Important note
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LATEX TikZposter
Fast Symmetry Detection with
Deep Learning and GeConv
PhD Candidate: T. Mkhoyan
Department: ASM
Supervisor 1st: Dr.ir.R. De Breuker
Supervisor 2nd: Dr.ir.C.Visser
Promotor Dr.ir.R. De Breuker
Contact: T.mkhoyan@tudelft.nl
Fast Symmetry Detection with
Deep Learning and GeConv
PhD Candidate: T. Mkhoyan
Department: ASM
Supervisor 1st: Dr.ir.R. De Breuker
Supervisor 2nd: Dr.ir.C.Visser
Promotor Dr.ir.R. De Breuker
Contact: T.mkhoyan@tudelft.nl
Aim
This study: Develop Fast 2-axis reflectional symmetry de-tection routine for estimation of aircraft wing orientation. Two methods developed and com-pared: traditional computer vision (GeConv ) versus pure Deep learning (RotNet ).
High level: Robust machine learning pipeline for prediction of wing deflection for aeroservoe-lastic control from raw images.
The Experiment
Markers Gust Wing oscillations
image u Cam 1 im ag e v Cam 2
Visual based model-free control
Visual model Flexible Aircraft Intelligent Controller Aeroelastic State Estimation (elastic states) input gust/turbulence visual frame [1088x600] output Real-time feedback
GeConv
Image filters and clustering (DBSCAN):
[1088x600x3] [1088x600x1] [1088x600x1] +Otsu
[1088x600x1] + Cluster
Sort and rotate points:
Pθhull = sort(P, sort(θcpk)); R = cos(θk) −sin(θk) sin(θk) cos(θk)
Geometric convolution and symmetry score: Pθk = (R · (θk−1 − θcp)T)T + θcp
θV symm =min(|mean(| sin(|θkcp|)|), θV symm) θHsymm =min(|mean(| cos(|θkcp|)|), θHsymm)
GeConv : Geometric Convolution process
Input [1088x600x3] HSV-BW [1088x600x3] BW img [1088x600x1] Contours [1088x600x1] Cluster [1088x600x1]
θ
p 0 200 400 600 800 1000 -100 0 100 200 300 400 500 600 700 800 0 30 60 90 120 150 180 210 240 270 300 330 0 100 200 300 400 minVsmm = 0.74991 minHsmm = 0.0067772 Vsmm = 0.25002 Hsmm = 0.96794 theta = 19.5 0 200 400 600 800 1000 -100 0 100 200 300 400 500 600 700 800 0 30 60 90 120 150 180 210 240 270 300 330 0 100 200 300 400 minVsmm = 0.48985 minHsmm = 0.0067772 Vsmm = 0.51015 Hsmm = 0.85998 theta = 39.5 0 200 400 600 800 1000 -100 0 100 200 300 400 500 600 700 800 0 30 60 90 120 150 180 210 240 270 300 330 0 100 200 300 400 minVsmm = 0.22648 minHsmm = 0.0067772 Vsmm = 0.77352 Hsmm = 0.63363 theta = 59.5 0 30 60 90 120 150 180 210 240 270 300 330 0 100 200 300 400 minVsmm = 0.0067772 minHsmm = 0.0067772 Vsmm = 0.99322 Hsmm = 0.11615 theta = 97 0 200 400 600 800 1000 -100 0 100 200 300 400 500 600 700 800DCNN structure
-θ[°] -0° -1° -2° CONV - RELUINPUT MAXPOOL CONV - RELU MAXPOOL FLATTEN FC SOFTMAX
... ... ... ...
Extract features Classify angle
DCNN: training Deep Convolutional Neural
Network
Initial orientation Rotated Orientation Corrected OrientationPlanning Smart-X
Current work with DCNN
Measured displacement Predicted displacement Ground truth Resnet-50 architecture Update weights Error metric/ Loss function ... ... ... Smart-X
