TMF-Net: Aircraft detection of remote sensing images using transformer and multi-scale fusion

Zheng Zhang; Jiahua Bai; Qing Tian

doi:10.1117/12.2634536

12 May 2022 TMF-Net: Aircraft detection of remote sensing images using transformer and multi-scale fusion

Zheng Zhang, Jiahua Bai, Qing Tian

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121731S (2022) https://doi.org/10.1117/12.2634536
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

Object detection is a research hot issue. With the improvement of remote sensing technology, the task of remote sensing target detection has become more important. Features of remote sensing aircraft images: small and dense targets to be measured. Common small object detection methods are implemented using CNN. In the small object detection task, since its convolution structure, information is lost. The Transformer's non-convolution structure can increase the receptive field while making full use of the image feature information, but because its attention calculation is between the tokens, the internal information of the token cannot be calculated. Therefore, we improve the encoder part and construct a scale fusion module to solve this problem. Experiments prove that the network proposed in the paper has good performance on the DIOR dataset.

Citation Download Citation

Zheng Zhang, Jiahua Bai, and Qing Tian "TMF-Net: Aircraft detection of remote sensing images using transformer and multi-scale fusion", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121731S (12 May 2022); https://doi.org/10.1117/12.2634536

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KEYWORDS

Computer programming

Transformers

Remote sensing

Target detection

Feature extraction

Image fusion

Convolution

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