A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion

Yong Zhang; Zhenke Qian; Junyan Cai

doi:10.62762/CJIF.2025.500710

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Volume 2, Issue 4, Chinese Journal of Information Fusion

Volume 2, Issue 4, 2025

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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

Yong Zhang 1,2

Zhenke Qian 1

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.

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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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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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TY  - JOUR
AU  - Zhang, Yong
AU  - Qian, Zhenke
AU  - Cai, Junyan
PY  - 2025
DA  - 2025/11/10
TI  - A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 2
IS  - 4
SP  - 313
EP  - 339
DO  - 10.62762/CJIF.2025.500710
UR  - https://www.icck.org/article/abs/CJIF.2025.500710
KW  - small obstacle detection
KW  - real-time detection
KW  - multidimensional information
KW  - multimodal fusion
KW  - lightweight models
AB  - 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.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Zhang2025A,
  author = {Yong Zhang and Zhenke Qian and Junyan Cai},
  title = {A Systematic Review on Real-time Detection of Small Obstacles Based on Multidimensional Information Fusion},
  journal = {Chinese Journal of Information Fusion},
  year = {2025},
  volume = {2},
  number = {4},
  pages = {313-339},
  doi = {10.62762/CJIF.2025.500710},
  url = {https://www.icck.org/article/abs/CJIF.2025.500710},
  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.},
  keywords = {small obstacle detection, real-time detection, multidimensional information, multimodal fusion, lightweight models},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}

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