基于深度度量学习的强泛化开关仪表识别算法
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深圳市科技创新委员会基础研究重点项目(JCYJ20200109114835623);深圳市科技创新委员会技术攻关重点项目(JSGG20220831105002004)

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Strong Generalization Switchgear Instrument Recognition Algorithm Based on Deep Metric Learning
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This work is supported by Shenzhen Science and Technology Innovation Commission (JCYJ20200109114835623, JSGG20220831105002004)

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    摘要:

    针对电厂开关检测方法无法应对现实开集环境,对稀有类别识别准确率低的现状,将目标识别问题转化为相似性度量问题,并提出新算法。新算法基于深度度量学习的三元组网络,利用加入 SE Block 的 ResNet-18 提取特征,并利用跨批次挖掘增强学习效果。为评估算法性能,创建了一个包含3 300 张开关图片的数据集,并使用新算法在该数据集上进行了闭集测试、开集测试、小样本测试。结果表明:新算法在闭集状态下具有良好的区分能力,不仅能准确识别训练集中的类别,还能有效区分训练时未遇到的及出现频率较低的状态。由此表明,该算法不仅适用于现实世界的开集环境,而且能显著提升对小样本数据的识别精度。

    Abstract:

    In response to the current power plant switch detection methods that are unable to cope with realworld open-set environments and the low accuracy in recognizing rare categories, the target recognition problem is transformed into a similarity measurement issue, and a new algorithm is proposed. The new algorithm is based on the triplet network of deep metric learning, using a ResNet-18 with an added SE Block to extract features, and enhances learning effects by cross-batch mining. To evaluate the performance of the algorithm, a dataset with 3 300 switch images was created. The algorithm was tested on the self-built dataset for closedset testing, open-set testing, and few-shot testing. The experimental results show that the algorithm demonstrates excellent discrimination ability in the closed-set state. It can not only accurately identify the categories in the training set but also effectively distinguish states that were not encountered during training and those with lower occurrence frequencies. This capability indicates that the algorithm is not only suitable for real-world open-set environments but also significantly improves the recognition accuracy for small-sample data.

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引文格式
冯天任,陈世峰.基于深度度量学习的强泛化开关仪表识别算法 [J].集成技术,2024,13(5):30-39

Citing format
FENG Tianren, CHEN Shifeng. Strong Generalization Switchgear Instrument Recognition Algorithm Based on Deep Metric Learning[J]. Journal of Integration Technology,2024,13(5):30-39

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  • 收稿日期:2024-02-05
  • 最后修改日期:2024-02-23
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  • 在线发布日期: 2024-09-24
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