Research on remote sensing image object detection algorithm based on deep learning

Li Lou; Fanfan He

doi:10.1117/12.3029760

21 June 2024 Research on remote sensing image object detection algorithm based on deep learning

Li Lou, Fanfan He

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 1316703 (2024) https://doi.org/10.1117/12.3029760
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

With the rapid development of computer vision technology, the application field of target detection is becoming higher and higher. With the continuous upgrading of UAV technology, the acquisition of remote sensing images has become more and more simple, the spatial resolution of remote sensing images has become higher and higher, and the information of images has become more abundant. There are many small targets in remote sensing images, the target size is quite different, and the background information is complex. In view of these problems, this paper proposes an improved YOLOv5 remote sensing image target detection algorithm based on YOLOv5 algorithm. Firstly, the backbone network of YOLOv5 is replaced by Swin Transformer, and the hierarchical feature map is constructed by using the displacement window, which effectively adapts to the computer vision task. Secondly, the SA (Shuffle Attention) attention mechanism is added to the network. The experimental results show that for the public DIOR dataset, the improved algorithm improves the detection accuracy by 1.7 % while maintaining the same detection speed as the original algorithm.

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

Citation Download Citation

Li Lou and Fanfan He "Research on remote sensing image object detection algorithm based on deep learning", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 1316703 (21 June 2024); https://doi.org/10.1117/12.3029760

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KEYWORDS

Remote sensing

Detection and tracking algorithms

Object detection

Target detection

Transformers

Deep learning

Computer vision technology

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