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A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion
Review Article | Published: 10 November 2025
Chinese Journal of Information Fusion Volume 2, Issue 4, 2025: 313-339
Volume 2, Issue 4, Chinese Journal of Information Fusion
Volume 2, Issue 4, 2025
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Deqiang Han
Deqiang Han
Xi'an Jiaotong University, China
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Chinese Journal of Information Fusion, Volume 2, Issue 4, 2025: 313-339

Open Access | Review Article | 10 November 2025
A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion
by
Yong Zhang Yong Zhang 1,2
Zhenke Qian Zhenke Qian 1
Junyan Cai Junyan Cai 3 *
1 College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
2 Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen 518060, China
3 Dongguan New-Generation Artificial Intelligence Industry Technology Research Institute, Dongguan 523000, China
* Corresponding Author: Junyan Cai, [email protected]
DOI: 10.62762/CJIF.2025.500710
Received: 05 June 2025, Accepted: 23 October 2025, Published: 10 November 2025  
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Abstract
Real-time detection of small obstacles is a critical challenge for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and mobile robots. These small obstacles (e.g., road debris, fallen branches, cables) pose significant safety risks due to their low visibility and irregular appearances. This paper presents a comprehensive systematic review of 117 technical articles published between 2016 and 2025, focusing on the techniques and deployment strategies for real-time small obstacle detection using fused multidimensional information. We summarize and analyze developments in small obstacle definitions, sensing hardware, detection algorithms, fusion methods, and real-time optimization techniques. Our findings reveal a growing trend toward integrating multiscale learning, multimodal fusion, and lightweight models for deployment in resource-constrained environments. However, challenges such as performance consistency across platforms, lack of standard definitions, and insufficient deployment practices persist. This review identifies future research opportunities and provides recommendations to guide further advances in robust, scalable, and deployable small obstacle detection systems.
Graphical Abstract
A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion
Keywords
small obstacle detection
real-time detection
multidimensional information
multimodal fusion
lightweight models
Data Availability Statement
Not applicable.
Funding
This work was supported by the Project of Shenzhen Science and Technology Plan under Grant KJZD20230923114405012 and Guangdong Provincial Key Laboratory under Grant 2023B1212060076.
Conflicts of Interest
Yong Zhang is an employee of Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen 518060, China, Junyan Cai is an employee of Dongguan New-Generation Artificial Intelligence Industry Technology Research Institute, Dongguan 523000, China. The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Zhang, Y., Qian, Z., & Cai, J. (2025). A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion. Chinese Journal of Information Fusion, 2(4), 313–339. https://doi.org/10.62762/CJIF.2025.500710
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