Application of YOLO11 Algorithm for Rotating Small Object Detection in Remote Sensing Images
Authors: 
Yang Shi, Yong Yang, Lei Xu, Yixiao Sun, Li Yu, Huiping DuAuthors Info & Claims
Yang Shi, Yong Yang, Lei Xu, Yixiao Sun, Li Yu, Huiping DuAuthors Info & ClaimsPages 79 - 84
Abstract
In recent years, object detection technology based on deep learning has developed rapidly. At present, the one-stage detection algorithm has been developed to YOLO11, which can not only carry out object detection more accurately, but also require fewer parameters and calculate faster. In this paper, based on the latest released YOLO11-OBB algorithm, remote sensing image data released in Tianzhi Cup competition was used to build and train small object dataset with algorithms of different parameter scales. In the end, the mAP of the trained YOLO11m-OBB model is improved by 1.93% compared with that of the YOLOv8m-OBB model. The number of parameters is reduced by 12.1%, enabling more accurate and efficient detection of certain types of rotating small object in complex remote sensing images.
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Index Terms
- Application of YOLO11 Algorithm for Rotating Small Object Detection in Remote Sensing Images
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Published In
November 2024
208 pages
ISBN:9798400711657
DOI:10.1145/3708360
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Published: 13 January 2025
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ICMML 2024
ICMML 2024: 2024 International Conference on Mathematics and Machine Learning
November 8 - 10, 2024
Nanjing, China
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