A Training Strategy of Flying Bird Object Detection Model Based on Improved Self-Paced Learning Algorithm-Reference-Cited by-同舟云学术

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A Training Strategy of Flying Bird Object Detection Model Based on Improved Self-Paced Learning Algorithm

Published:2024-03 Issue: Volume:44 Page:444-450
ISSN:
Container-title:Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
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Author:

Sun Ziwei¹^ORCID,Hua Zexi¹^ORCID,Li Hengchao¹^ORCID

Affiliation:

1. School of Information Science and Technology, Southwest Jiaotong University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3672919.3673000

Reference13 articles.

1. X. Shi, J. Hu, X. Lei and S. Xu, "Detection of Flying Birds in Airport Monitoring Based on Improved YOLOv5," 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2021, pp. 1446-1451.

2. A new skeleton based flying bird detection method for low-altitude air traffic management

3. H. Zhao, D. Cai, Z. Liang and Y. Wang, "An Improved Method for Farm Birds Detection Based on YOLOv5s," 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Shanghai, China, 2022, pp. 183-187.

4. Shivam Goel Santosh Bhusal Matthew E Taylor and Manoj Karkee "Detection and localization of birds for Bird Deterrence using UAS " 2017 ASABE Annual International Meeting 1701288. (doi:10.13031/aim.201701288).

5. Bird detection and species classification with time-lapse images around a wind farm: Dataset construction and evaluation

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