Remote sensing image object detection based on improved YOLOv8

Pengnan Tian; Ying Nie

doi:10.1117/12.3038539

23 August 2024 Remote sensing image object detection based on improved YOLOv8

Pengnan Tian, Ying Nie

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 1325015 (2024) https://doi.org/10.1117/12.3038539
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Aiming at the issues of low object detection accuracy and false detection of small object in complex background, a modified remote sensing image detection algorithm based on YOLOv8 is proposed. Use of the RFA convolution kernel enhances the extraction of network space features and their associated global information, and further bring in the Deformable Attention mechanism to obtain a more flexible dynamic attention model. The Powerful-IoU loss function is introduced in the regression loss function, which improves the convergence speed of the network and the object detection accuracy. On the RSOD and DOTAv1.0 datasets the mAP of the improved algorithm is increased by 1.5% and 2.3% respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengnan Tian and Ying Nie "Remote sensing image object detection based on improved YOLOv8", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 1325015 (23 August 2024); https://doi.org/10.1117/12.3038539

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