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Review
. 2023 Sep 25;9(10):194.
doi: 10.3390/jimaging9100194.

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Daniel Vera-Yanez  1 , António Pereira  2   3 , Nuno Rodrigues  2 , José Pascual Molina  1   4 , Arturo S García  1   4 , Antonio Fernández-Caballero  1   4
Affiliations
Review

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Daniel Vera-Yanez et al. J Imaging. .

Abstract

This paper presents a systematic review of articles on computer-vision-based flying obstacle detection with a focus on midair collision avoidance. Publications from the beginning until 2022 were searched in Scopus, IEEE, ACM, MDPI, and Web of Science databases. From the initial 647 publications obtained, 85 were finally selected and examined. The results show an increasing interest in this topic, especially in relation to object detection and tracking. Our study hypothesizes that the widespread access to commercial drones, the improvements in single-board computers, and their compatibility with computer vision libraries have contributed to the increase in the number of publications. The review also shows that the proposed algorithms are mainly tested using simulation software and flight simulators, and only 26 papers report testing with physical flying vehicles. This systematic review highlights other gaps to be addressed in future work. Several identified challenges are related to increasing the success rate of threat detection and testing solutions in complex scenarios.

Keywords: computer vision; midair collision; obstacle detection; systematic review.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Search process.
Figure 2
Figure 2
Publications over the years.
Figure 3
Figure 3
Categorization of papers by camera type.
Figure 4
Figure 4
Number of papers using each vision recognition method.
Figure 5
Figure 5
Combination of vision recognition processes in paper.
Figure 6
Figure 6
Categorization of papers by test method.
Figure 7
Figure 7
Categorization of papers using physical equipment by aerial vehicle.
Figure 8
Figure 8
Use of multirotor UAVs over the years.
Figure 9
Figure 9
Use of simulation over the years.
See this image and copyright information in PMC

References

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