基于STM32微控制器的MCUVLM-RWKV视觉-语言模型研究OA

With the widespread application of artificial intelligence in fields such as security,industry,and agriculture,the demand for edge devices on vision reasoning tasks continues to grow.However,due to hardware constraints,deployment schemes of vision-language models designed for STM32 microcontrollers remain relatively scarce.To address this problem,this paper proposes an STM32-oriented vision-language model,MCUVLM-RWKV.The model integrates three core modules:a lightweight vision encoder,a lightweight vision feature mapper,and an RWKV decoder with a dual-mode operation mechanism,enabling image captioning tasks.Experimental results show that under the memory and storage limitations of STM32,MCUVLM-RWKV outperforms several mainstream models in evaluation metrics such as BLEU-4,ROUGE-L,and METEOR.Specifically,the ROUGE-L score reaches 55.7,which is significantly higher than that of other comparative models,indicating stronger modeling capability in long-sequence reasoning tasks.In addition,MCUVLM-RWKV demonstrates excellent performance in terms of parameter scale and inference memory consumption,further verifying its reasoning efficiency and deployment feasibility in MCU scenarios.