Research advanced in object detection based on deep learning

Research Article
Open access

Research advanced in object detection based on deep learning

Dongyang Li 1*
  • 1 Xi’an International University, Xi’an, 710077, China    
  • *corresponding author 190123020011@post.xaiu.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

In the field of computer vision, identifying specific objects in images and predicting their location and class have long been hot research topics.Algorithms for early object detection rely on traditional handcrafted features, and the speed and precision of their detection cannot meet the needs of real-world applications. Rapid development in convolutional neural networks has accelerated the development of object identification systems based on deep learning.Existing deep learning-based object identification algorithms mostly use two-stage detection and single-stage detection, according to various detection frameworks. In this paper, around the above two types of frameworks, the latest research progress in the field of object detection is systematically introduced. Specifically, we first introduce representative object detection algorithms, including their design ideas, basic processes, and advantages and disadvantages. Second, using widely-used datasets, we objectively compare the effectiveness of several detection techniques. Finally, we summarize the unsolved problems in the detection of objects and talk about the route this topic will take going forward.

Keywords:

Object Detection, Deep Learning, Multi-class, Lightweighting.

Li,D. (2023). Research advanced in object detection based on deep learning. Applied and Computational Engineering,5,603-608.
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References

[1]. L. Kalake, W. Wan and L. Hou, "Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: A Review," in IEEE Access, vol. 9, pp. 32650-32671, 2021.

[2]. B. Liu, W. Zhao and Q. Sun, "Study of object detection based on Faster R-CNN," 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 6233-6236.

[3]. Y. Liu, "An Improved Faster R-CNN for Object Detection," 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2018, pp. 119-123.

[4]. X. Xiao and X. Tian, "Research on Reference Target Detection of Deep Learning Framework Faster-RCNN," 2021 5th Annual International Conference on Data Science and Business Analytics (ICDSBA), Changsha, China, 2021, pp. 41-44.

[5]. S. Widiyanto, D. T. Wardani and S. Wisnu Pranata, "Image-Based Tomato Maturity Classification and Detection Using Faster R-CNN Method," 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2021, pp. 130-134.

[6]. L. Li and Y. Liang, "Deep Learning Target Vehicle Detection Method Based on YOLOv3-tiny," 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021, pp. 1575-1579.

[7]. J. Fan, J. Lee, I. Jung and Y. Lee, "Improvement of Object Detection Based on Faster R-CNN and YOLO," 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jeju, Korea (South), 2021, pp. 1-4.

[8]. M. Zhang, T. Wang, W. Zhao, X. Chen and J. Wan, "Research on Target Detection of Excavator in Aerial Photography Environment based on YOLOv4," 2020 International Conference on Robots & Intelligent System (ICRIS), Sanya, China, 2020, pp. 711-714.

[9]. L. Xiaomeng, F. Jun and C. Peng, "Vehicle Detection in Traffic Monitoring Scenes Based on Improved YOLOV5s," 2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), Shijiazhuang, China, 2022, pp. 467-471.

[10]. S. Bouraya and A. Belangour, "Approaches to Video Real time Multi-Object Tracking and Object Detection: A survey," 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia, 2021, pp. 145-151.

Cite this article

Li,D. (2023). Research advanced in object detection based on deep learning. Applied and Computational Engineering,5,603-608.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. L. Kalake, W. Wan and L. Hou, "Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: A Review," in IEEE Access, vol. 9, pp. 32650-32671, 2021.

[2]. B. Liu, W. Zhao and Q. Sun, "Study of object detection based on Faster R-CNN," 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 6233-6236.

[3]. Y. Liu, "An Improved Faster R-CNN for Object Detection," 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2018, pp. 119-123.

[4]. X. Xiao and X. Tian, "Research on Reference Target Detection of Deep Learning Framework Faster-RCNN," 2021 5th Annual International Conference on Data Science and Business Analytics (ICDSBA), Changsha, China, 2021, pp. 41-44.

[5]. S. Widiyanto, D. T. Wardani and S. Wisnu Pranata, "Image-Based Tomato Maturity Classification and Detection Using Faster R-CNN Method," 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2021, pp. 130-134.

[6]. L. Li and Y. Liang, "Deep Learning Target Vehicle Detection Method Based on YOLOv3-tiny," 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021, pp. 1575-1579.

[7]. J. Fan, J. Lee, I. Jung and Y. Lee, "Improvement of Object Detection Based on Faster R-CNN and YOLO," 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jeju, Korea (South), 2021, pp. 1-4.

[8]. M. Zhang, T. Wang, W. Zhao, X. Chen and J. Wan, "Research on Target Detection of Excavator in Aerial Photography Environment based on YOLOv4," 2020 International Conference on Robots & Intelligent System (ICRIS), Sanya, China, 2020, pp. 711-714.

[9]. L. Xiaomeng, F. Jun and C. Peng, "Vehicle Detection in Traffic Monitoring Scenes Based on Improved YOLOV5s," 2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), Shijiazhuang, China, 2022, pp. 467-471.

[10]. S. Bouraya and A. Belangour, "Approaches to Video Real time Multi-Object Tracking and Object Detection: A survey," 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia, 2021, pp. 145-151.

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