With the development of the times,traditional image recognition algorithms have certain limitations in terms of their computational ability and effectiveness when facing a large number of complex images.In view of this,the study first analyzes the ex-isting convolutional neural network in deep learning and improves its computational aspects.Secondly,a field programmable gate ar-ray gas pedal was added for optimization.Finally,the layout is implemented in an embedded system and a novel image recognition model is proposed.The experimental results show that the proposed new model has the highest recognition accuracy of 93％,the low-est Loss value of 0.4,and the fastest iteration time of 200 times.Its shortest processing time for a single image is 11.5 ms,and the average resource utilization is 80.5％.The overall recognition rate of this model in the flower image simulation test is 83.3％,with the highest recognition rate of 92.3％for the rose category.In summary,the new model proposed in the study is able to efficiently ac-complish image recognition tasks in embedded environments,while providing accurate and efficient image recognition solutions for subsequent technological research.
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