Abstract

Process control in manufacturing industries usually lacks flexibility and adaptability. The process planning is traditionally pursued within the production scheduling and then remains unchanged until a major overhaul is necessary. Consequently, no process knowledge acquired by the machine operators is used to adapt, and thus improve, the process control. In this paper, a fully automated process control solution for container logistics is proposed, which is based on deep neural networks and has been trained from process steering decisions made by employees. Further, a fully automated framework for the labeling of container images is introduced, making use of inherent properties of the logistic process. This allows to automatically generate data sets without the need for manual labeling by an operator.

References

References
1.
Wang
,
S.
,
Wan
,
J.
,
Li
,
D.
, and
Zhang
,
C.
,
2016
, “
Implementing Smart Factory of Industrie 4.0: An Outlook
,”
Int. J. Distrib. Sen. Netw.
,
12
(
1
), pp.
3159805:1
3159805:10
. 10.1155/2016/3159805
2.
Islam
,
T.
,
Mukhopadhyay
,
S. C.
, and
Suryadevara
,
N. K.
,
2017
, “
Smart Sensors and Internet of Things: A Postgraduate Paper
,”
IEEE Sen. J.
,
17
(
3
), pp.
577
584
. 10.1109/JSEN.2016.2630124
3.
Xu
,
X.
,
2012
, “
From Cloud Computing to Cloud Manufacturing
,”
Robot. Comput. Integr. Manuf.
,
28
(
1
), pp.
75
86
. 10.1016/j.rcim.2011.07.002
4.
Chen
,
M.
,
Mao
,
S.
, and
Liu
,
Y.
,
2014
, “
Big Data: A Survey
,”
Mob. Netw. Appl.
,
19
(
2
), pp.
171
209
. 10.1007/s11036-013-0489-0
5.
Meijer
,
B. R.
,
1992
, “
Rules and Algorithms for the Design of Templates for Template Matching
,”
Proceedings. 11th IAPR International Conference on Pattern Recognition
,
The Hague, Netherlands
,
Aug. 30–Sept. 3
, pp.
760
763
.
6.
Harris
,
C. G.
, and
Stephens
,
M.
,
1988
, “
A Combined Corner and Edge Detector
,”
Proceedings of Fourth Alvey Vision Conference
,
Manchester, UK
,
Aug. 31–Sept. 2
, pp.
147
151
.
7.
Chen
,
H.
,
Cui
,
Y.
,
Qiu
,
R.
,
Chen
,
P.
,
Liu
,
W.
, and
Liu
,
K.
,
2018
, “
Image-Alignment Based Matching for Irregular Contour Defects Detection
,”
IEEE Access
,
6
, pp.
68749
68759
. 10.1109/ACCESS.2018.2879861
8.
Kotsiantis
,
S.
,
2007
, “Supervised Machine Learning: A Review of Classification Techniques,”
Emerging Artificial Intelligence Applications in Computer Engineering - Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
, Vol.
160
,
IOS Press
,
Amsterdam, Netherlands
, pp.
3
24
.
9.
Raina
,
R.
,
Madhavan
,
A.
, and
Ng
,
A.
,
2009
, “
Large-scale Deep Unsupervised Learning Using Graphics Processors
,”
Proceedings of the 26th International Conference on Machine Learning
,
Montreal, Canada
,
June 14–18
, pp.
873
880
.
10.
Kaelbling
,
L. P.
,
Littman
,
M.
, and
Moore
,
A.
,
1996
, “
Reinforcement Learning: A Survey
,”
J. Artif. Intel. Res.
,
4
(
1
), pp.
237
285
.
11.
Viola
,
P.
, and
Jones
,
M.
,
2004
, “
Robust Real-Time Face Detection
,”
Int. J. Comput. Vis.
,
57
(
2
), pp.
137
154
. 10.1023/B:VISI.0000013087.49260.fb
12.
Dalal
,
N.
, and
Triggs
,
B.
,
2005
, “
Histograms of Oriented Gradients for Human Detection
,”
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
,
San Diego, CA
,
June 20–25
, pp.
886
893
.
13.
Suga
,
A.
,
Fukuda
,
K.
,
Takiguchi
,
T.
, and
Ariki
,
Y.
,
2008
, “
Object Recognition and Segmentation Using Sift and Graph Cuts
,”
19th International Conference on Pattern Recognition
,
Tampa, FL
,
Dec. 8–11
, pp.
1
4
.
14.
Girshick
,
R.
,
Donahue
,
J.
,
Darrell
,
T.
, and
Malik
,
J.
,
2014
, “
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
,”
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition
,
Columbus, OH
,
June 24–27
, pp.
580
587
.
15.
He
,
K.
,
Zhang
,
X.
,
Ren
,
S.
, and
Sun
,
J.
,
2014
, “
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
,”
Proceedings of the European Conference on Computer Vision
,
Zurich, Switzerland
,
Sept. 6–12
, pp.
346
361
.
16.
Girshick
,
R.
,
2015
, “
Fast R-CNN
,”
Proceedings of the IEEE International Conference on Computer Vision
,
Santiago, Chile
,
Dec. 11–18
, pp.
1440
1448
.
17.
Ren
,
S.
,
He
,
K.
,
Girshick
,
R.
, and
Sun
,
J.
,
2015
, “
Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks
,”
Advances in Neural Information Processing Systems
,
Montreal, Canada
,
Dec. 7–12
, pp.
91
99
.
18.
Szegedy
,
C.
,
Vanhoucke
,
V.
,
Ioffe
,
S.
,
Shlens
,
J.
, and
Wojna
,
Z.
,
2016
, “
Rethinking the Inception Architecture for Computer Vision
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Las Vegas, NV
,
June 27–30
, pp.
2818
2826
.
19.
Yoo
,
D.
,
Park
,
S.
,
Lee
,
J. Y.
,
Paek
,
A.
, and
Kweon
,
I. S.
,
2015
, “
Attentionnet: Aggregating Weak Directions for Accurate Object Detection
,”
Proceedings of the IEEE International Conference on Computer Vision
,
Santiago, Chile
,
Dec. 11–18
, pp.
2659
2667
.
20.
Redmon
,
J.
,
Divvala
,
S.
,
Girshick
,
R.
, and
Farhadi
,
A.
,
2016
, “
You Only Look Once: Unified, Real-Time Object Detection
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Las Vegas, NV
,
June 27–30
, pp.
779
788
.
21.
Liu
,
W.
,
Anguelov
,
D.
,
Erhan
,
D.
,
Szegedy
,
C.
,
Reed
,
S.
,
Fu
,
C. Y.
, and
Berg
,
A.
,
2016
, “
SSD: Single Shot Multibox Detector
,”
Proceedings of the European Conference on Computer Vision
,
Amsterdam, Netherlands
,
Oct. 8–16
, pp.
21
37
.
22.
Redmon
,
J.
, and
Farhadi
,
A.
,
2017
, “
YOLO9000: Better, Faster, Stronger
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Honolulu, HI
,
July 21–26
, pp.
6517
6525
.
23.
Lin
,
T.-Y.
,
Maire
,
M.
,
Belongie
,
S.
,
Hays
,
J.
,
Perona
,
P.
,
Ramanan
,
D.
,
Dollár
,
P.
, and
Zitnick
,
C. L.
,
2014
, “
Microsoft COCO: Common Objects in Context
,”
Proceedings of the European Conference on Computer Vision
,
Zurich, Switzerland
,
Sept. 6–12
, pp.
740
755
.
24.
Russakovsky
,
O.
,
Deng
,
J.
,
Su
,
H.
,
Krause
,
J.
,
Satheesh
,
S.
,
Ma
,
S.
,
Huang
,
Z.
,
Karpathy
,
A.
,
Khosla
,
A.
,
Bernstein
,
M.
,
Berg
,
A. C.
, and
Fei-Fei
,
L.
,
2015
, “
ImageNet Large Scale Visual Recognition Challenge
,”
Int. J. Comput. Vis.
,
115
(
3
), pp.
211
252
. 10.1007/s11263-015-0816-y
25.
Everingham
,
M.
,
Eslami
,
A. S. M.
,
Van Gool
,
L.
,
Williams
,
C. K. I.
,
Winn
,
J.
, and
Zisserman
,
A.
,
2015
, “
The Pascal Visual Object Classes Challenge: A Retrospective
,”
Int. J. Comput. Vis.
,
111
(
1
), pp.
98
136
. 10.1007/s11263-014-0733-5
26.
Huang
,
J.
,
Rathod
,
V.
,
Sun
,
C.
,
Zhu
,
M.
,
Korattikara
,
A.
,
Fathi
,
A.
,
Fischer
,
I.
,
Wojna
,
Z.
,
Song
,
Y.
,
Guadarrama
,
S.
, and
Murphy
,
K.
,
2017
, “
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
,”
IEEE Conference on Computer Vision and Pattern Recognition
,
Honolulu, HI
,
July 21–26
, pp.
3296
3297
.
27.
Zhao
,
Z.-Q.
,
Zheng
,
P.
,
Xu
,
S.-T.
, and
Wu
,
X.
,
2019
, “
Object Detection With Deep Learning: A Review
,”
IEEE Trans. Neural Netw. Learn. Sys.
,
30
(
11
), pp.
3212
3232
. 10.1109/TNNLS.2018.2876865
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