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2025 | OriginalPaper | Buchkapitel

A State-of-the-Art Review of Deep Learning-Based Object Detection Methods and Techniques

verfasst von : Chhaya Gupta, Nasib Singh Gill, Preeti Gulia

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

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Abstract

To locate and recognize things in a picture or a video is the aim of object recognition. Before the development of deep learning, object recognition required several processes. Many commonplace applications, including security systems, video surveillance systems, self-driving cars, and guides for the blind and visually handicapped, are built on deep learning and object detection. This paper offers a comprehensive review of deep learning-based item identification approaches, covering various object detection methodologies, deep learning-based object recognition frameworks, and specific model attributes. In addition to several other models like VGGNet, ResNet, DenseNet, and AlexNet, this paper also covers the construction and operation of the Convolutional Neural Network (CNN). The underlying history of object detection is also covered in this essay, which focuses on object recognition applications such as face and human detection, weapon recognition, and pedestrian identification. A detailed summary is also presented that may help to serve further enhancement in the related domain of research.

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Literatur
1.
Zurück zum Zitat Adel, A., & Elhagry, G. Investigating the challenges of class imbalance and scale variation in object detection in aerial images, 1–8. Adel, A., & Elhagry, G. Investigating the challenges of class imbalance and scale variation in object detection in aerial images, 1–8.
6.
Zurück zum Zitat Chotikunnan, P., Puttasakul, T., Chotikunnan, R., et al. (2023). Evaluation of single and dual image object detection through image segmentation using ResNet18 in robotic vision applications. Journal of Robotics Control, 4, 263–277. https://doi.org/10.18196/jrc.v4i3.17932 Chotikunnan, P., Puttasakul, T., Chotikunnan, R., et al. (2023). Evaluation of single and dual image object detection through image segmentation using ResNet18 in robotic vision applications. Journal of Robotics Control, 4, 263–277. https://​doi.​org/​10.​18196/​jrc.​v4i3.​17932
8.
Zurück zum Zitat Farooq, M., & Hafeez, A. (2020). COVID-ResNet: A deep learning framework for screening of COVID19 from radiographs. Farooq, M., & Hafeez, A. (2020). COVID-ResNet: A deep learning framework for screening of COVID19 from radiographs.
9.
Zurück zum Zitat Fujitake, M., & Sugimoto, A. Video representation learning through prediction for online object detection, 530–539. Fujitake, M., & Sugimoto, A. Video representation learning through prediction for online object detection, 530–539.
16.
Zurück zum Zitat Han, G., Zhang, X., & Li, C. (2017). Revisiting faster R-CNN: A deeper look at region proposal network. Lecture Notes Computer Science (Including Subseries Lecture Notes Artificial Intelligence Lecture Notes in Bioinformatics), 10636. LNCS, 14–24. https://doi.org/10.1007/978-3-319-70090-8_2 Han, G., Zhang, X., & Li, C. (2017). Revisiting faster R-CNN: A deeper look at region proposal network. Lecture Notes Computer Science (Including Subseries Lecture Notes Artificial Intelligence Lecture Notes in Bioinformatics), 10636. LNCS, 14–24. https://​doi.​org/​10.​1007/​978-3-319-70090-8_​2
31.
Zurück zum Zitat Lee, D.-H., Chen, K.-L., Liou, K.-H., et al. (2019). Deep learning and control algorithms of direct perception for autonomous driving, 2–7. Lee, D.-H., Chen, K.-L., Liou, K.-H., et al. (2019). Deep learning and control algorithms of direct perception for autonomous driving, 2–7.
32.
Zurück zum Zitat Li, G., Liu, Z., Member, S., & Bai, Z. Lightweight salient object detection in optical remote sensing images via feature correlation, 1–11. Li, G., Liu, Z., Member, S., & Bai, Z. Lightweight salient object detection in optical remote sensing images via feature correlation, 1–11.
35.
Zurück zum Zitat Md, A., & Alam, M. (2019). Towards pedestrian detection using RetinaNet in ECCV 2018 wider pedestrian detection challenge. Md, A., & Alam, M. (2019). Towards pedestrian detection using RetinaNet in ECCV 2018 wider pedestrian detection challenge.
36.
37.
Zurück zum Zitat Neapolitan, R. E., & Neapolitan, R. E. (2018). Neural networks and deep learning. Neapolitan, R. E., & Neapolitan, R. E. (2018). Neural networks and deep learning.
38.
Zurück zum Zitat Olmos, R., Tabik, S., Perez-Hernandez, F., et al. (2021). MULTICAST: MULTI confirmation-level alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance. Olmos, R., Tabik, S., Perez-Hernandez, F., et al. (2021). MULTICAST: MULTI confirmation-level alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance.
47.
Zurück zum Zitat Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd international conference on learning representation ICLR 2015—Conference tracking proceeding (1–14). Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd international conference on learning representation ICLR 2015—Conference tracking proceeding (1–14).
52.
55.
Zurück zum Zitat Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey, 1–39. Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey, 1–39.
Metadaten
Titel
A State-of-the-Art Review of Deep Learning-Based Object Detection Methods and Techniques
verfasst von
Chhaya Gupta
Nasib Singh Gill
Preeti Gulia
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-4152-6_35