Ze Ren
ABSTRACT
Space infrared sensors have become the "eyes" of space activities, there is currently no established identification method applied to the embedded deployment of space infrared sensor, despite it being a crucial device for aerospace detection due to the influence of deep learning target detection model embedded deployment difficulty, large sample demand, model parameter weight, low computing efficiency, and other factors. In this paper, the YOLOv5 detection model's network structure and activation function are lightened up. Next, the algorithm model's adaptive learning rate optimization algorithm is used to build the lightened-up YOLOv5 infrared image target recognition model. And the image samples were preprocessed with the intention of addressing the poor image quality brought on by background interference and multi-pose acquisition. Then, a method of sample amplification is suggested in light of the issues with the deep learning model. Consequently, the deep learning lightweight model applied in this study is one-third smaller than the conventional model, which dramatically increases target detection speed without significantly lowering detection rate. In addition to investigates ideas for the embedded deployment and recognition of infrared images in small sample space.
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Index Terms
- Lightweight recognition method of infrared sensor image based on deep learning method
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