Image classification system and method based on image recognition technology

Through dynamic preprocessing, multi-scale feature fusion and edge computing optimization, the accuracy and robustness of the image classification system in complex scenarios is solved, and the energy efficiency of the anti-sample defense and edge devices is improved.

CN120451667APending Publication Date: 2025-08-08CHONGQING CREATION VOCATIONAL COLLEGE +1
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Patent Information

Application Number
CN202510552927.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The classification performance of existing image classification systems has significantly decreased in low-light or high-noise scenarios, lacks the coordinated utilization of fine-grained local features and global semantic information, and most systems lack defense capabilities for adversarial sample attacks, and lack of dynamic resource allocation mechanisms, resulting in inefficiency of edge devices.

Method used

Dynamic selection preprocessing strategies, parallel convolution paths and feature pyramid structures are adopted to enhance multi-scale characterization capabilities, integrate adversarial sample detection and defense mechanisms, and introduce edge computing optimization technology to achieve dynamic scheduling and hybrid accuracy acceleration of computing resources.

Benefits of technology

It improves the classification accuracy and robustness of the image classification system in complex scenarios, enhances the ability to defend against samples, and optimizes the computing efficiency and credibility of edge devices.

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Abstract

The invention relates to the technical field of image recognition, in particular to an image classification system and method based on the image recognition technology, and the system comprises an image collection module which is used for obtaining original image data to be classified; the preprocessing module is used for carrying out denoising, normalization and size standardization processing on the image; the feature extraction module is used for extracting multi-level features of the image by adopting a deep convolutional neural network; the classification decision module is used for weighting fusion features based on an attention mechanism and outputting a classification result; the output module is used for displaying the classification labels and confidence scores; according to the method, the input quality is optimized by dynamically selecting a preprocessing strategy, the multi-scale representation capability is enhanced by adopting a parallel convolution path and a feature pyramid structure, the robustness of the system is improved by integrating an adversarial sample detection and defense mechanism, and the dynamic scheduling and mixing precision acceleration of computing resources are realized by introducing an edge computing optimization technology. And the operation efficiency is obviously improved on the premise of ensuring the classification precision.
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