Abstract
During the last years, a noticeable growth is observed in the field of computer vision research. In computer vision, object detection is a task of classifying and localizing the objects in order to detect the same. The widely used object detection applications are human–computer interaction, video surveillance, satellite imagery, transport system, and activity recognition. In the wider family of deep learning architectures, convolutional neural network (CNN) made up with set of neural network layers is used for visual imagery. Deep CNN architectures exhibit impressive results for detection of objects in digital image. This paper represents a comprehensive review of the recent development in object detection using convolutional neural networks. It explains the types of object detection models, benchmark datasets available, and research work carried out of applying object detection models for various applications.
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
References
Z, Zhao, P. Zheng, S. Xu, X. Wu, Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)
L. Liu, W. Ouyang, X. Wang et al., Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128, 261–318 (2020)
A. Opelt, A. Pinz, M. Fussenegger, P. Auer, Generic object recognition with boosting. IEEE TPAMI 28(3), 416–431 (2006)
A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 1–13 (2018)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015)
H.A. Rowley, S. Baluja, T. Kanade, Neural network-based face detection. PAMI (1998)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
A.R. Pathak, M. Pandey, S. Rautaray, Application of deep learning for object detection. Procedia Comput. Sci. 132, 1706–1717 (2018)
C. Li, Transfer learning with Mask R-CNN, https://medium.com/@c_61011/transfer-learning-with-mask-r-cnn-f50cbbea3d29
R, Girshick, J, Donahue, T, Darrell, J, Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
L. Weng, Object detection for dummies part 3: R-CNN family. https://lilianweng.github.io/lil-log/2017/12/31/object-recognition-for-dummies-part-3.html
R. Girshick, Fast R-CNN, in ICCV, pp. 1440–1448 (2015)
M, Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman, The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 303–338 (2010)
S. Ren, K. He, R. Girshick, J. Sun, Faster RCNN: towards real time object detection with region proposal networks. IEEE TPAMI 39(6), 1137–1149 (2017)
K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask RCNN, in ICCV (2017)
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real time object detection, in CVPR, pp. 779–788 (2016)
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A. Berg, SSD: single shot multibox detector, in ECCV, pp. 21–37 (2016)
J. Deng, W. Dong, R. Socher, L. Li, K. Li, F. Li, ImageNet: a large scale hierarchical image database, in CVPR, pp. 248–255 (2009)
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, L. Zitnick, Microsoft COCO: common objects in context. in ECCV, pp. 740–755 (2014)
M. Everingham, S. Eslami, L.V. Gool, C. Williams, J. Winn, A. Zisserman, The pascal visual object classes challenge: a retrospective. IJCV 111(1), 98–136 (2015)
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset et al., The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. arXiv:1811.00982. (2018)
W. You, L. Chen, Z. Mo, Soldered dots detection of automobile door panels based on faster R-CNN model, in Chinese Control And Decision Conference (CCDC) (Nanchang, China, 2019), pp. 5314–5318
W. Wu, Y. Yin, X. Wang, D. Xu, Face detection with different scales based on faster R-CNN. IEEE Trans. Cybern. 49(11), 4017–4028 (2019)
T. Liu, T. Stathaki, Faster R-CNN for robust pedestrian detection using semantic segmentation network. Front. Neurorobot. (2018)
R. Anantharaman, M. Velazquez, Y. Lee, Utilizing mask R-CNN for detection and segmentation of oral diseases, in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Madrid, Spain, 2018), pp. 2197–2204
G. Cao, W. Song, Z. Zhao, Gastric cancer diagnosis with mask R-CNN, in 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (Hangzhou, China, 2019), pp. 60–63
M. Bizjak, P. Peer, Ž. Emeršič, Mask R-CNN for ear detection, in 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (Opatija, Croatia, 2019), pp. 1624–1628
T. Santad, P. Silapasupphakornwong, W. Choensawat, K. Sookhanaphibarn, Application of YOLO deep learning model for real time abandoned baggage detection, in IEEE 7th Global Conference on Consumer Electronics (GCCE) (Nara, 2018), pp. 157–158
H. Nguyen, Improving faster R-CNN framework for fast vehicle detection. Math. Prob. Eng. 1–11 (2019)
N. Xuan, D. Mengyang, D. Haoxuan, H. Bingliang, W. Edward, Attention mask R-CNN for ship detection and segmentation from remote sensing images. IEEE Access 1–1 (2020)
Z. Krawczyk, J. Starzyński, Bones detection in the pelvic area on the basis of YOLO neural network, in 19th International Conference (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patel, S., Patel, A. (2021). Object Detection with Convolutional Neural Networks. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_52
Download citation
DOI: https://doi.org/10.1007/978-981-15-7106-0_52
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7105-3
Online ISBN: 978-981-15-7106-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)