A survey study in Object Detection: A Comprehensive Analysis of Traditional and State-of-the-Art Approaches

Authors

  • Marwa A. Hameed Department of Computer Science, College of Computer Science & Information Technology, University of Basrah, Basrah, Iraq
  • Zainab A. Khalaf Department of Computer Science, College of education for Pure Sciences, University of Basrah, Basrah, Iraq.

DOI:

https://doi.org/10.56714/bjrs.50.1.5

Keywords:

Object Detection, Deep Learning, Traditional Detectors, Image Object Detection

Abstract

Object detection is an essential field within computer vision, focusing on identifying objects' presence and category within image or video data. The significance of this issue is paramount in numerous domains that directly impact people's lives, including autonomous driving, healthcare systems, and security monitoring. In contrast to traditional methodologies employed for object detection, deep learning-based algorithms have demonstrated substantial progress in computational efficiency and precision in recent years. This study aims to provide a comprehensive review of object detection by methodically employing deep learning to facilitate a comprehensive and in-depth comprehension of the fundamental principles in this field. The discussion has encompassed various subjects, such as the obstacles and complexities associated with object detection and the traditional and deep learning detectors. The detection of objects within images and videos, the real-time detection of objects, detection of 3D objects, commonly used datasets, and the metrics employed for evaluating object detection performance. This study will likely yield scientific benefits for academics working in the field of object detection and deep learning.

Downloads

Download data is not yet available.

References

M. Ahmed, K. A. Hashmi, A. Pagani, M. Liwicki, D. Stricker, and M. Z. Afzal, “Survey and performance analysis of deep learning based object detection in challenging environments,” Sensors, vol. 21, no. 15. 2021. Doi: https://doi.org/10.3390/s21155116.

L. Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019, Doi: https://doi.org/10.1109/ACCESS.2019.2939201.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587, 2014,Doi:https://doi.org/10.1109/CVPR.2014.81.

Y. Xiao et al., “A review of object detection based on deep learning,” Multimedia Tools and Applications, vol. 79, no. 33–34, pp. 23729–23791, 2020, Doi:https://doi.org/10.1007/s11042-020-08976-6.

M. Jiang, S. Huang, J. Duan, and Q. Zhao, “Salicon: Saliency in context,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1072–1080.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.

W. Liu et al., “SSD: Single shot multibox detector,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016, Doi: https://doi.org/10.1007/978-3-319-46448-0_2.

Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, “Object Detection in 20 Years: A Survey,” Proceedings of the IEEE, 2023, Doi: https://doi.org/10.1109/JPROC.2023.3238524.

H. Zhang and X. Hong, “Recent progresses on object detection: a brief review,” Multimedia Tools and Applications, vol. 78, no. 19. pp. 27809–27847, 2019. Doi:https://doi.org/10.1007/s11042-019-07898-2.

A. Dhillon and G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,” Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85–112, 2020.

X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, 2020, Doi:https://doi.org/10.1016/j.neucom.2020.01.085.

L. Aziz, M. S. B. H. Salam, U. U. Sheikh, and S. Ayub, “Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review,” IEEE Access, vol. 8, pp. 170461–170495, 2020,Doi:https://doi.org/10.1109/ACCESS.2020.3021508.

S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A survey of modern deep learning based object detection models,” Digital Signal Processing: A Review Journal, vol. 126. 2022. Doi: https://doi.org/10.1016/j.dsp.2022.103514.

J. Wang, T. Zhang, Y. Cheng, and N. Al-Nabhan, “Deep learning for object detection: A survey,” Computer Systems Science and Engineering, vol. 38, no. 2, pp. 165–182, 2021,Doi:https://doi.org/10.32604/CSSE.2021.017016.

Z. Li et al., “Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey,” Remote Sensing, vol. 14, no. 10, pp. 1–41, 2022, Doi:https://doi.org/10.3390/rs14102385.

J. Kaur and W. Singh, “Tools, techniques, datasets and application areas for object detection in an image: a review,” Multimedia Tools and Applications, vol. 81, no. 27. pp. 38297–38351, 2022. Doi:https://doi.org/10.1007/s11042-022-13153-y.

L. Liu et al., “Deep Learning for Generic Object Detection: A Survey,” International Journal of Computer Vision, vol. 128, no. 2, pp. 261–318, 2020,Doi:https://doi.org/10.1007/s11263-019-012474.

Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Systems with Applications, vol. 172, no. April 2020, p. 114602, 2021, Doi:https://doi.org/10.1016/j.eswa.2021.114602.

K. Tong, Y. Wu, and F. Zhou, “Recent advances in small object detection based on deep learning: A review,” Image and Vision Computing, vol. 97, p. 103910, 2020, Doi:https://doi.org/10.1016/j.imavis.2020.103910.

M. Abdulla and A. Marhoon, “Agriculture based on Internet of Things and Deep Learning,” Iraqi Journal for Electrical and Electronic Engineering, vol. 18, no. 2, pp. 1–8, 2022, Doi: https://doi.org/10.37917/ijeee.18.2.1.

R. S. Khudeyer and N. M. Al-moosawi, “Fake Image Detection Using Deep Learning EfficientNet-V2 network,” vol. 47, pp. 115–120, 2023.

N. Odey and A. Marhoon, “Feature Deep Learning Extraction Approach for Object Detection in Self-Driving Cars,” 2023.

G. Jin, R. I. Taniguchi, and F. Qu, “Auxiliary Detection Head for One-Stage Object Detection,” IEEE Access, vol. 8. pp. 85740–85749, 2020.Doi:https://doi.org/10.1109/ACCESS.2020.2992532.

K. Zhao and X. Ren, “Small Aircraft Detection in Remote Sensing Images Based on YOLOv3,” IOP Conference Series: Materials Science and Engineering, vol. 533, no. 1, 2019, Doi: https://doi.org/10.1088/1757-899X/533/1/012056.

R. Girshick, “Fast R-CNN,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 Inter, pp. 1440–1448, 2015,Doi:https://doi.org/10.1109/ICCV.2015.169.

Y. Wang et al., “Remote sensing image super-resolution and object detection: Benchmark and state of the art,” Expert Systems with Applications, vol. 197. 2022.Doi:https://doi.org/10.1016/j.eswa.2022.116793.

P. Singh, M. Diwakar, A. Shankar, R. Shree, and M. Kumar, “A Review on SAR Image and its Despeckling,” Archives of Computational Methods in Engineering, vol. 28, no. 7, pp. 4633–4653, 2021, Doi: https://doi.org/10.1007/s11831-021-09548-z.

L. Tang, W. Tang, X. Qu, Y. Han, W. Wang, and B. Zhao, “A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images,” Remote Sensing, vol. 14, no. 4, pp. 1–24, 2022, Doi:https://doi.org/10.3390/rs14040973.

Y. Zhao, L. Zhao, Z. Liu, D. Hu, G. Kuang, and L. Liu, “Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery,” Jan. 2022,Doi: https://doi.org/10.1109/TGRS.2021.3139994.

H. K. Jung and G. S. Choi, “Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions,” Applied Sciences (Switzerland), vol. 12, no. 14, 2022, Doi:https://doi.org/10.3390/app12147255.

X. Zhu, S. Lyu, X. Wang, and Q. Zhao, “TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2021-Octob, pp. 2778–2788, 2021, Doi: https://doi.org/10.1109/ICCVW54120.2021.00312

R. Walambe, A. Marathe, and K. Kotecha, “Multiscale object detection from drone imagery using ensemble transfer learning,” Drones, vol. 5, no. 3, p. 66, 2021,Doi:https://doi.org/10.3390/drones5030066.

R. Zhang, Z. Miao, Q. Zhang, S. Hao, and S. Wang, “Video Object Detection by Aggregating Features across Adjacent Frames,” Journal of Physics: Conference Series, vol. 1229, no. 1, 2019, doi: 10.1088/1742-6596/1229/1/012039.

H. Zhu, H. Wei, B. Li, X. Yuan, and N. Kehtarnavaz, “A review of video object detection: Datasets, metrics and methods,” Applied Sciences (Switzerland), vol. 10, no. 21. pp. 1–24, 2020. Doi:https://doi.org/10.3390/app10217834.

S. Jha, C. Seo, E. Yang, and G. P. Joshi, “Real time object detection and trackingsystem for video surveillance system,” Multimedia Tools and Applications, vol. 80, no. 3, pp. 3981–3996, 2021, Doi:https://doi.org/10.1007/s11042-020-09749-x.

L. Jiao et al., “New generation deep learning for video object detection: A survey,” IEEE Transactions on Neural Networks and Learning Systems, 2021.

V. V, C. R. K, and R. A. C., “Real Time Object Detection System with YOLO and CNN Models: A Review,” vol. XIV, no. 7, pp. 144–151, 2022.

S. Heo, S. Cho, Y. Kim, and H. Kim, “Real-Time Object Detection System with Multi-Path Neural Networks,” Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, vol. 2020-April, pp. 174–187, 2020,Doi:https://doi.org/10.1109/RTAS48715.2020.000-8.

T. Yin, X. Zhou, and P. Krähenbühl, “Center-based 3D Object Detection and Tracking,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, no. Figure 1, pp. 11779–11788, 2021,Doi:https://doi.org/10.1109/CVPR46437.2021.01161.

X. Pan, Z. Xia, S. Song, L. E. Li, and G. Huang, “3D Object Detection with Pointformer,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 7459–7468, 2021,Doi:https://doi.org/10.1109/CVPR46437.2021.00738.

J. Mao, S. Shi, X. Wang, and H. Li, “3D Object Detection for Autonomous Driving: A Comprehensive Survey,” International Journal of Computer Vision, no. February, 2023, Doi:https://doi.org/10.1007/s11263-023-01790-1.

L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories,” Computer Vision and Image Understanding, vol. 106, no. 1, pp. 59–70, 2007, Doi:https://doi.org/10.1016/j.cviu.2005.09.012.

A. S. Rao and K. Mahantesh, “Learning Semantic Features for Classifying Very Large Image Datasets Using Convolution Neural Network,” SN Computer Science, vol. 2, no. 3, pp. 1–9, 2021, Doi:https://doi.org/10.1007/s42979-021-00589-6.

S. H. S. Basha, S. K. Vinakota, V. Pulabaigari, S. Mukherjee, and S. R. Dubey, “AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning,” Neural Networks, vol. 133, pp. 112–122, 2021,Doi:https://doi.org/10.1016/j.neunet.2020.10.009.

G. Griffin, a Holub, and P. Perona, “Caltech-256 object category dataset,” Caltech mimeo, vol. 11, no. 1, p. 20, 2007.

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010,Doi:https://doi.org/10.1007/s11263-009-0275-4.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015,Doi:https://doi.org/10.1007/s11263-015-0816-y.

T. Y. Lin et al., “Microsoft COCO: Common objects in context,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8693 LNCS, no. PART 5, pp. 740–755, 2014,Doi:https://doi.org/10.1007/978-3-319-10602-1_48.

A. Kuznetsova et al., “The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale,” International Journal of Computer Vision, vol. 128, no. 7, pp. 1956–1981, 2020, Doi:https://doi.org/10.1007/s11263-020-01316-z.

Z. A. Khalaf, S. S. Hammadi, A. K. Mousa, H. M. Ali, H. R. Alnajar, and R. H. Mohsin, “Coronavirus disease 2019 detection using deep features learning.,” International Journal of Electrical & Computer Engineering (2088-8708), vol. 12, no. 4, 2022.

G. S. Ohannesian and E. J. Harfash, “Epileptic Seizures Detection from EEG Recordings Based on a Hybrid System of Gaussian Mixture Model and Random Forest Classifier,” Informatica (Slovenia), vol. 46, no. 6, pp. 501–505, 2022,Doi:https://doi.org/10.31449/inf.v46i6.4203.

Downloads

Published

30-06-2024

How to Cite

Hameed, M. A., & Khalaf, Z. A. (2024). A survey study in Object Detection: A Comprehensive Analysis of Traditional and State-of-the-Art Approaches. Basrah Researches Sciences, 50(1), 16. https://doi.org/10.56714/bjrs.50.1.5

Issue

Section

Articles