Multi-scale image recognition strategy based on convolutional neural network


  • Huajun Zhang
  • Su Diao
  • Yining Yang
  • Jiachen Zhong
  • Yafeng Yan



Convolutional neural networks, Multi-scale feature, Image recognition strategies, Computer vision


The accurate recognition and interpretation of multi-scale visual information is a critical focus within contemporary computer vision research. To this end, this study explores and innovatively constructs a multi-scale image recognition strategy based on a Convolutional Neural Network (CNN) with a multi-level and multi-resolution perception domain. This strategy is embedded with an advanced multi-level convolutional operation mechanism, which enables the model to intelligently explore and learn the multi-scale feature representation space of images from tiny texture to grand structure, from shallow simple features to deep semantic abstraction. The core technology path of this paper is to design a deep separable convolutional architecture and combine pyramid pool technology to form a unique network module. This modular design not only ensures the computational efficiency of the model but also improves the ability of extracting and integrating multi-scale image features. Following intensive experimentation on an array of extensively recognized and substantial image datasets, the multi-scale image recognition approach introduced in our study has demonstrated marked enhancements in both recognition capability and stability, manifesting clear superiority compared to conventional, single-scale image recognition methodologies. This research not only enriches the theoretical framework of image recognition, but also provides a new and efficient solution for dealing with complex multi-scale image recognition challenges in practical applications, and further promotes the development of image understanding and recognition technology.


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How to Cite

Zhang, H., Diao, S., Yang, Y., Zhong, J., & Yan, Y. (2024). Multi-scale image recognition strategy based on convolutional neural network. Journal of Computing and Electronic Information Management, 12(3), 107-113.

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