With the rapid development of deep learning in the field of image processing, aircraft detection technology has become a hot spot in computer vision research. In this context, this paper proposes an airplane detection model based on improved Swin-Transformer. The model mainly integrates two advanced techniques: the attention mechanism of ECA (Efficient Channel Attention) and the multi-scale module of BiFPN (Bidirectional Feature Pyramid Network).The introduction of the ECA attention mechanism enables the model to more accurately capture and emphasize the aircraft features of key channel information, thus enhancing the recognition of aircraft targets. Meanwhile, the application of BiFPN optimizes the model's performance in dealing with aircraft targets at different scales, which is especially more effective in detecting small or long-range aircraft. A series of experiments on standard aircraft detection datasets show that our improved model achieves 98.3% and 97.6% in terms of accuracy and recall, respectively, compared to the traditional Swin-Transformer model. These results not only demonstrate the value of the ECA attention mechanism and BiFPN in aircraft detection, but also provide a new direction for future Transformer-based target detection research.


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    Title :

    Aircraft inspection based on improved Swin Transformer


    Contributors:
    Wei, Yonghe (editor) / Liu, Fengli (editor) / Feng, Li (author) / Jun, Jia (author)

    Conference:

    International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023) ; 2023 ; Shenyang, China


    Published in:

    Proc. SPIE ; 13071


    Publication date :

    2024-02-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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