Object Detection Techniques: A Review
DOI:
https://doi.org/10.31185/wjcms.165Keywords:
object detection, R-CNN, Fast R-CNN, Faster R-CNN, Mesh R-CNN, Mask R-CNN.Abstract
Humans can understand their surroundings clearly because they regularly notice objects in their environment. It is essential for the machine to perceive the surroundings similarly to how humans do in order to make it autonomous and capable of navigating in the human world. The machine can assess its surroundings and identify objects using object detection. This can simplify a number of tasks and enable the machine to recognize its surroundings. Making bounding boxes that surround the objects is essentially how object detection systems work to locate objects in an image. Object detection has applications such as autonomous robot navigation, surveillance, face detection, and vehicle
References
M. Wu, “Object detection based on RGC mask R-CNN,” IET Image Process, vol. 14, no. 8, pp. 1502–1508, 2020.
G. A. Montazer and D. Giveki, “Content based image retrieval system using clustered scale invariant feature transforms,” Optik (Stuttg), vol. 126, no. 18, pp. 1695–1699, 2015
L. Shi and J. H. Lv, “Face detection system based on AdaBoost algorithm,” Appl. Mech. Mater, vol. 380, no. 4, pp. 3917–3920, 2013.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 779–788, 2016.
W. Liu, “SSD: Single shot multibox detector,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), pp. 21–37, 2016.
B. Mahaur, N. Singh, and K. K. Mishra, “Road object detection: a comparative study of deep learning-based algorithms,” Multimed. Tools Appl, vol. 81, no. 10, pp. 14247–14282, 2022.
N. Yadav and U. Binay Comparative Study of Object Detection Algorithms, pp. 586–591, 2017.
L. Du, R. Zhang, and X. Wang, “Overview of two-stage object detection algorithms,” J. Phys. Conf. Ser, vol. 1544, no. 1, 2020.
S. Nie, Z. Jiang, H. Zhang, B. Cai, and Y. Yao, “Inshore ship detection based on mask r-cnn,” Int. Geosci. Remote Sens. Symp, pp. 693 696, 2018.
Z. Yang, Y. Yuan, M. Zhang, X. Zhao, Y. Zhang, and B. Tian, “Safety distance identification for crane drivers based on mask r-cnn,” Sensors (Switzerland), vol. 19, no. 12, 2019.
M. Maity, S. Banerjee, and S. S. Chaudhuri, “Faster R-CNN and YOLO based Vehicle detection: A Survey,” Proc. - 5th Int, vol. 2021, pp. 1442– 1447, 2021.
J. W. Johnson Adapting Mask-RCNN for Automatic Nucleus Segmentation, pp. 1–7, 2018.
B. Xu, “Automated cattle counting using Mask R-CNN in quadcopter vision system,” Comput. Electron. Agric, vol. 171, pp. 105300–105300, 2020.
K. Zhao, “Deep Learning-based Building Labeling 3.1. Mask R-CNN for Initial Polygon Generation,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work, pp. 247–251, 2018.
D. Zhao and H. Li, “Forward vehicle detection based on deep convolution neural network,” AIP Conf. Proc, vol. 2073, 2019.
R. Padilla, S. L. Netto, E. A. B. Da, and Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” Int. Conf. Syst. Signals, Image Process, pp. 237–242, 2020.
Z. Zou, Z. Shi, Y. Guo, and J. Ye Object Detection in 20 Years: A Survey, 2019.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 580–587, 2014.
R. Girshick, “Fast R-CNN,” Proc. IEEE Int. Conf. Comput. Vis, pp. 1440–1448, 2015.
B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, “High Performance Visual Tracking with Siamese Region Proposal Network,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 8971–8980, 2018.
A. Salvador, X. Gir, and F. Marqu, “Faster R-CNN Features for Instance Search,” IEEE Xplore, pp. 9–16, 2013.
Y. W. Chao, S. Vijayanarasimhan, B. Seybold, D. A. Ross, J. Deng, and R. Sukthankar, “Faster R-CNN for Temporal Action Localization,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1130–1139, 2018.
C. Lee, H. J. Kim, and K. W. Oh, “Comparison of faster R-CNN models for object detection,” Int. Conf. Control. Autom. Syst, vol. 0, no. Iccas, pp. 107–110, 2016.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 42, no. 2, pp. 386–397, 2020.
T. Vu, T. Bao, Q. V. Hoang, C. Drebenstetd, P. V. Hoa, and H. H. Thang, “Measuring blast fragmentation at Nui Phao open-pit mine, Vietnam using the Mask R-CNN deep learning model,” Min. Technol. Trans. Inst. Min. Metall, vol. 130, no. 4, pp. 232–243, 2021.
Z. Yang, R. Dong, H. Xu, and J. Gu Instance segmentation method based on improved mask R-cnn for the stacked electronic components, vol. 9, 2020.
Z. Zhou, Q. Lai, S. Ding, and S. Liu, “Joint 2D object detection and 3D reconstruction via adversarial fusion mesh r-cnn,” Proc. - IEEE Int. Symp. Circuits Syst, pp. 0–4, 2021.
Y. Wu, “Monocular Instance Level 3D Object Reconstruction based on Mesh R-CNN,” Proc. - 2020 5th Int. Conf, vol. 2020, pp. 1–6, 2020.
G. Gkioxari, F. Ai, . . M. R-Cnn, and I. Xplore pp. 9785–9795, 2020
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