Few-shot pointer meters detection method based on meta-learning

doi:10.11996/JG.j.2095-302X.2025010081

Abstract

Abstract:

The accuracy of meters location is a critical factor in ensuring the accuracy of meters recognition. However, it is challenging to collect instrument data in complex industrial scenarios, existing pointer instrument detection methods exhibit low detection accuracy and poor real-time performance in few-shot situations. For this reason, the Sparse-Meta-DETR method was proposed for few-shot pointer meter detection based on meta-learning. Inspired by the object detection model Meta-DETR, this method adopted the meta-learning strategy. During the meta-training stage, few-shot tasks were created to train the Sparse-Meta-DETR model, enhancing metrics ability of the correlational aggregation module for support set and query set classes in the feature space. This enabled the model to recognize classes present in images during the few-shot training stage with few-shot tasks, quickly adapt to few-shot tasks with novel classes, detect pointer meters in complex industrial scenarios. A lightweight backbone network, Efficientnet b1, was introduced as the feature extractor to reduce the computational complexity and parameter of the model, thereby improving the detection speed. Simultaneously, a scoring network was designed as a token sparsification sampler, creating a sparsification mask to select foreground features from query features. This guided the Transformer encoders and decoders to focus on foreground features, thereby reducing computational complexity of few-shot training stage and improving detection accuracy. The Sparse-Meta-DETR model achieved an AP⁵⁰of 94.2% and an AP⁷⁵ of 87.5% in 20-shot task, and an AP⁵⁰ of 91.1% in 10-shot tasks. Compared to the baseline model, the improved model reduced time complexity by 74.5%. Experimental results demonstrated that the Sparse-Meta-DETR can not only effectively ensure the accuracy of pointer meter positioning detection but also improve the real-time performance in the case of few-shot. Its overall performance surpassed other few-shot deep-learning algorithms such as Meta RCNN.

Key words: pointer meter, meta-learning, few-shot, object detection, sparsification

CLC Number:

SUN Qianlai, LIN Shaohang, LIU Dongfeng, SONG Xiaoyang, LIU Jiayao, LIU Ruizhen. Few-shot pointer meters detection method based on meta-learning[J]. Journal of Graphics, 2025, 46(1): 81-93.

Figures/Tables 12

References 37

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方法	代表模型	特点
基于元学习的方法	Meta-RCNN^[16] Meta-DETR^[18]	检测精度高，适用于样本数量极少的任务
基于迁移学习的方法	CFA-DeFRCN^[14]	易于实现，但不适用于样本数量极少的任务
基于数据增强的方法	LVC^[13]	易于实现，但实时性较差，定位精度较低
基于度量学习的方法	CME^[15] ARSML^[19]	适用于样本数量极少的任务，但定位精度低

方法	代表模型	特点
基于元学习的方法	Meta-RCNN^[16] Meta-DETR^[18]	检测精度高，适用于样本数量极少的任务
基于迁移学习的方法	CFA-DeFRCN^[14]	易于实现，但不适用于样本数量极少的任务
基于数据增强的方法	LVC^[13]	易于实现，但实时性较差，定位精度较低
基于度量学习的方法	CME^[15] ARSML^[19]	适用于样本数量极少的任务，但定位精度低

Stage	卷积层	输入尺寸	通道数	层数
1	Conv 3×3	224×224	32	1
2	MBConv1, k3×3	112×112	16	1
3	MBConv6, k3×3	112×112	24	2
4	MBConv6, k3×3	56×56	40	2
5	MBConv6, k3×3	28×28	80	3
6	MBConv6, k3×3	14×14	112	3
7	MBConv6, k3×3	14×14	192	4
8	MBConv6, k3×3	7×7	320	1
9	Conv 1×1 & Pooling & FC	7×7	1280	1

Stage	卷积层	输入尺寸	通道数	层数
1	Conv 3×3	224×224	32	1
2	MBConv1, k3×3	112×112	16	1
3	MBConv6, k3×3	112×112	24	2
4	MBConv6, k3×3	56×56	40	2
5	MBConv6, k3×3	28×28	80	3
6	MBConv6, k3×3	14×14	112	3
7	MBConv6, k3×3	14×14	192	4
8	MBConv6, k3×3	7×7	320	1
9	Conv 1×1 & Pooling & FC	7×7	1280	1

操作名称	输入尺寸	输出尺寸	层数
Layer normalization	l×d	l×d	1
Linear layer & GELU	l×d	l×d	1
Linear layer & GELU	l×d	l×d/2	1
Linear layer & GELU	l×d/2	l×d/4	1
Linear layer	l×d/4	l×1	1