Abstract
Remote sensing image has the characteristics of large and complex data volume, which makes the traditional remote sensing image object detection technology difficult to meet the current demand. As artificial intelligence achieves more and more achievements, the application of object detection technology based on deep learning in remote sensing image is becoming more and more widely applied. In this paper, based on YOLOv3 object detection algorithm, the collected data is effectively expanded according to the remote sensing image objective feature, and multiple training and verification tests are carried out. According to experimental results, the proposed remote sensing image object detection model can effectively eliminate the impurity pixel amount with high accuracy, and can improve the quality of object detection.
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Acknowledgements
This work was supported by the 41001251, U1804153(National Natural Science Foundation of China); 202102110115, 212102210502(The Science and Technology Development Project of Henan Province); 2021C01GX018, 2021C01GX020(The Science and Technology Development Project of Anyang).
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Song, X., Zhou, H., Feng, X. (2023). Research on Remote Sensing Image Object Detection Based on Deep Learning. In: Kountchev, R., Nakamatsu, K., Wang, W., Kountcheva, R. (eds) Proceedings of the World Conference on Intelligent and 3-D Technologies (WCI3DT 2022). Smart Innovation, Systems and Technologies, vol 323. Springer, Singapore. https://doi.org/10.1007/978-981-19-7184-6_39
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DOI: https://doi.org/10.1007/978-981-19-7184-6_39
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