Lightweight bird recognition method based on mobileNet-CBAM model
For bird recognition tasks,the effect of some current deep learning network models is not ade-quate.Based on the pain points of the existing models,a lightweight bird recognition method with more ad-vantages in recognition accuracy,computing performance,resource consumption and practicability is pro-posed.This method is based on the lightweight MobileNet model and blends the Convolutional Block Atten-tion Module(CB AM),which reduces the complexity of the model while enhancing the ability to extract lo-cal features.The comparative experiment with the mainstream model verifies the validity of the model.In addition,the model can be applied to hardware platforms such as robots and portable mobile phones,ex-panding the application scenarios,adding databases for auxiliary calculations,reducing additional costs,and having both portability and practicability.
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