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复杂系统与复杂性科学  2025, Vol. 22 Issue (3): 65-72    DOI: 10.13306/j.1672-3813.2025.03.009
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于混合卷积神经网络特征增强的目标识别算法
赵文炎1, 钟诚1, 田殿雄1, 卢泽钰1, 李勇2
1.国网冀北电力有限公司唐山供电公司,河北 唐山 063000;
2.上海洲固电力科技有限公司,上海 200000
Target Recognition Algorithm Based on Hybrid Convolutional Neural Network Feature Enhancement
ZHAO Wenyan1, ZHONG Cheng1, TIAN Dianxiong1, LU Zeyu1, LI Yong2
1. Tangshan Power Supply Company of State Grid Jibei Electric Power Co, Tangshan 263000, China;
2. Shanghai Zhougu Power Technology Co, Ltd, Shanghai 200000, China
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摘要 为克服传统目标识别算法应对复杂场景时,特征提取能力不足的问题,提出了一种新的基于混合卷积神经网络的目标识别算法。该算法的核心在于将非欧几里得域的学习能力与传统卷积神经网络相结合,从而增强关键特征表达的深度和广度。该算法能够提取并强化目标识别中的关键特征信息,显著提升识别的准确性和鲁棒性。
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赵文炎
钟诚
田殿雄
卢泽钰
李勇
关键词 混合卷积神经网络特征表达目标识别    
Abstract:To overcome the problem of insufficient feature extraction capabilities of traditional target recognition algorithms in complex scenarios, a new target recognition algorithm based on a hybrid convolutional neural network is proposed. The core of the algorithm lies in combining the learning capability of non-Euclidean domains with traditional convolutional neural networks to enhance the depth and breadth of feature representation. The algorithm in this paper can extract and strengthen the key feature information in target recognition, and significantly improve the accuracy and robustness of recognition.
Key wordshybrid convolutional neural network    feature representation    target recognition
收稿日期: 2024-09-02      出版日期: 2025-10-09
ZTFLH:  TB3  
  U411  
作者简介: 赵文炎(1985-),男,湖北仙桃人,硕士研究生,主要研究方向为物资管理、轨迹预测。
引用本文:   
赵文炎, 钟诚, 田殿雄, 卢泽钰, 李勇. 基于混合卷积神经网络特征增强的目标识别算法[J]. 复杂系统与复杂性科学, 2025, 22(3): 65-72.
ZHAO Wenyan, ZHONG Cheng, TIAN Dianxiong, LU Zeyu, LI Yong. Target Recognition Algorithm Based on Hybrid Convolutional Neural Network Feature Enhancement[J]. Complex Systems and Complexity Science, 2025, 22(3): 65-72.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2025.03.009      或      https://fzkx.qdu.edu.cn/CN/Y2025/V22/I3/65
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