Abstract
Changing transport regulations for intralogistics tasks leads to the need for object detection of hazard labels on parcels in high-resolution grayscale images. For this reason, this paper compares different Convolutional Neural Network (CNN) based object detection systems. Specifically, a YOLO implementation known as Darkflow as well as a self-developed Object Detection Pipeline (ODP) based on the Inception V3 model is considered. Different datasets consisting of synthetic and real images are created to set up the necessary training and evaluation environments. To check the robustness of the systems under real operation conditions, they are assessed by the mean Average Precision (mAP) metric. Moreover, results are evaluated to answer various questions like the impact of synthetic data during training, or the highest quality level of the systems. The YOLO models showed a higher mAP and a much higher detection speed than the MSER Object Detection Pipeline at the cost of higher training times. The mixed training data set with synthetic and real data showed a slightly reduced mAP on the validation set compared to just real data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Darkflow, URL: https://github.com/thtrieu/darkflow (Retrieved: 2019-06-19).
- 2.
Nathan Silberman and Sergio Guadarrama, TensorFlow-Slim image classification model library, URL: https://github.com/tensorflow/models/tree/master/research/slim (Retrieved: 2019-06-19).
References
A. Canziani, A. Paszke, E. Culurciello, An analysis of deep neural network models for practical applications (2016)
S. Cesare, Y. Xiang, Software Similarity and Classification (Springer, Berlin, 2012), p. 67
A. Chachra, P. Mehndiratta, M. Gupta, Sentiment analysis of text using deep convolution neural networks, in 10th International Conference on Contemporary Computing (IC3) (2017), pp. 1–6
Y.H. Chang, P.L. Chung, H.W. Lin, Deep learning for object identification in ROS-based mobile robots, in IEEE International Conference on Applied System Invention (ICASI) (2018), pp. 66–69
M. Everingham, S.M.A. Eslami, L.V. Gool, C.K.I. Williams, J. Winn, A. Zisserman, The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. (IJCV) 111(1), 98–136 (2015)
Y. Fan, X. Lu, D. Li, Y. Liu, Video-based emotion recognition using CNN-RNN and C3D hybrid networks, in 18th ACM International Conference on Multimodal Interaction, ICMI ’16 (2016), pp. 445–450
R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014), pp. 580–587
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016)
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014)
R. Grzeszick, J.M. Lenk, F. Moya Rueda, G.A. Fink, S. Feldhorst, M. ten Hompel, Deep neural network based human activity recognition for the order picking process, in 4th International Workshop on Sensor-based Activity Recognition and Interaction, iWOAR ’17 (2017) pp. 1–6
A. Gupta, A. Vedaldi, A. Zisserman, Synthetic data for text localisation in natural images, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 2315–2324
M. Himstedt, E. Maehle, Online semantic mapping of logistic environments using RGB-D cameras. Int. J. Adv. Robot. Syst. 14(4), 113 (2017)
R. Hou, C. Chen, M. Shah. Tube convolutional neural network (T-CNN) for action detection in videos, in IEEE International Conference on Computer Vision (ICCV) (2017), pp. 5823–5832
IATA DGR, 2017 Lithium Battery Guidance Document. https://www.iata.org/whatwedo/cargo/dgr/Documents/lithium-battery-guidance-document-2017-en.pdf (2016) (Retrieved: 2019-06-19)
H. Jo, Y. H. Na, J.B. Song, Data augmentation using synthesized images for object detection, in 17th International Conference on Control, Automation and Systems (ICCAS) (2017), pp. 1035–1038
T.A. Le, A.G. Baydin, R. Zinkov, F. Wood, Using synthetic data to train neural networks is model-based reasoning, in International Joint Conference on Neural Networks (IJCNN) (2017), pp. 3514–3521
A.R. Ludwig, H. Piorek, A.H. Kelch, D. Rex, S. Koitka, C.M. Friedrich, Improving model performance for plant image classification with filtered noisy images, in Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, vol. 1866 (2017)
J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, in British Machine Vision Conference (BMVC 2002), vol. 22 (BMVA Press, 2002), pp. 384–393
Y. Mednikov, S. Nehemia, B. Zheng, O. Benzaquen, D. Lederman, Transfer representation learning using inception-V3 for the detection of masses in mammography, in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018), pp. 2587–2590
A. Mikołajczyk, M. Grochowski, Data augmentation for improving deep learning in image classification problem, in International Interdisciplinary PhD Workshop (IIPhDW) (2018), pp. 117–122
Y. Nagaoka, T. Miyazaki, Y. Sugaya, S. Omachi, Text detection by faster R-CNN with multiple region proposal networks, in 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 06 (2017), pp. 15–20
K. Noda, Y. Yamaguchi, K. Nakadai, Hiroshi G. Okuno, T. Ogata, Audio-visual speech recognition using deep learning. Appl. Intell. 42(4), 722–737 (2015)
G. Nowacki, C. Krysiuk, R. Kopczewski, Dangerous goods transport problems in the European Union and Poland. TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 10(1), 143–150 (2016)
S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
D. Pande, C. Sharma, V. Upadhyaya, Object detection and path finding using monocular vision, in International Conference on Signal Propagation and Computer Technology (ICSPCT) (2014), pp. 376–379
P. Ramakrishna, E. Hassan, R. Hebbalaguppe, M. Sharma, G. Gupta, L. Vig, G. Sharma, G. Shroff, An ar inspection framework: feasibility study with multiple ar devices, in IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) (2016), pp. 221–226
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 779–788
J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 6517–6525
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in Advances in Neural Information Processing Systems 28 (NIPS) (2015), pp. 91–99
R. Rothe, M. Guillaumin, L. Van Gool, Non-maximum suppression for object detection by passing messages between windows, in Computer Vision – ACCV 2014 (Springer International Publishing, 2015), pp. 290–306
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, F. Li, ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)
Y. Sawada, Y. Sato, T. Nakada, S. Yamaguchi, K. Ujimoto, N. Hayashi, Improvement in classification performance based on target vector modification for all-transfer deep learning. Appl. Sci. 9(1) (2019)
M. Schwarze, A. Milan, A.S. Periyasamy, S. Behnke, RGB-D object detection and semantic segmentation for autonomous manipulation in clutter. Int. J. Robot. Res. 37(4–5), 437–451 (2017)
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 2818–2826
S. Tsutsui, D. Crandall, A data driven approach for compound figure separation using convolutional neural networks, in The IAPR International Conference on Document Analysis and Recognition (ICDAR) (2017)
J. Wang, S. Liu, Q. Yang, Transfer learning for air traffic control LVCSR system, in Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (2017), pp. 169–172
M.A. Wani, F.A. Bhat, S. Afzal, A. Khan, Advances in Deep Learning (Springer, Berlin, 2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Schweigert, A., Blesing, C., Friedrich, C.M. (2020). Deep Learning Based Hazard Label Object Detection for Lithium-ion Batteries Using Synthetic and Real Data. In: Wani, M., Kantardzic, M., Sayed-Mouchaweh, M. (eds) Deep Learning Applications. Advances in Intelligent Systems and Computing, vol 1098. Springer, Singapore. https://doi.org/10.1007/978-981-15-1816-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-1816-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1815-7
Online ISBN: 978-981-15-1816-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)