Progress of Object Detection Algorithms Based on Deep Learning, Lightweight Optimization, and Cross-Domain Applications

Yayu Cai

doi:10.54254/2755-2721/2026.TJ29143

Applied and Computational EngineeringOpen access

Progress of Object Detection Algorithms Based on Deep Learning, Lightweight Optimization, and Cross-Domain Applications

Research Article

Open Access

Progress of Object Detection Algorithms Based on Deep Learning, Lightweight Optimization, and Cross-Domain Applications

Yayu Cai ^1*

¹ College of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, 519087, China

^*Corresponding author: s230026004@mail.uic.edu.cn

Published on 5 November 2025

ACE Vol.203

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-515-8

ISBN (Online): 978-1-80590-516-5

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Abstract

As one of the core tasks in the field of computer vision, object detection has made significant progress over the last few years, driven by deep learning technology. This paper systematically reviews the development process of object detection algorithms, from traditional methods to modern deep learning methods, focusing on the technical characteristics and performance of representative algorithms such as the You Only Look Once (YOLO) series, Region-Based Convolutional Neural Networks (R-CNN) series, and the Single-Shot MultiBox Detector (SSD). In this paper, the lightweight optimization strategy is discussed in depth to deal with difficulties of computing resource limitations in practical applications of object detection, including network structure simplification, computing efficiency optimization, and hardware adaptation. In addition, this paper further combines typical cases, such as a two-stage efficient parking space detection method, to expound the application status and challenges of object detection technology in cross-field fields such as autonomous driving, intelligent transportation, and industrial testing. Finally, this paper looks forward to the future development direction of object detection technology, and puts forward potential research hotspots such as multimodal fusion and edge computing adaptation.

Keywords:

Object Detection, Deep Learning, Computer Vision

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References

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References

[3]. Jiang, J., Tang, R., Kang, W., Xu, Z., & Qian, C. (2025). Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision. Applied Sciences, 15(3), 1004.

[8]. Hou, X. (2025). Object detection and applications based on deep learning. Innovation and Application of Science and Technology, 15(8), 21–26.

[10]. Wang, N., & Zhi, M. (2025). A review on research on general object detection algorithms in deep learning. Computer Science and Exploration, 19(5), 1115–1140.

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Cite this article

Cai,Y. (2025). Progress of Object Detection Algorithms Based on Deep Learning, Lightweight Optimization, and Cross-Domain Applications. Applied and Computational Engineering,203,28-33.

Data availability

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

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-515-8(Print) / 978-1-80590-516-5(Online)

Editor: Marwan Omar, Guozheng Rao

Conference website: https://www.confspml.org/tianjin.html

Conference date: 21 December 2025

Series: Applied and Computational Engineering

Volume number: Vol.203

ISSN: 2755-2721(Print) / 2755-273X(Online)

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