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Pointer meters recognition method in the wild based on innovative deep learning techniques.

Jiajun Feng¹, Haibo Luo², Rui Ming³

¹College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Scientific Reports

|January 4, 2025

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This study introduces a deep learning model for accurately identifying industrial meter pointers in low-quality images. The novel approach enhances recognition accuracy and efficiency in complex industrial settings.

Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Accurate meter reading is crucial in industrial settings.
Low-quality images pose challenges for traditional recognition methods.
Existing deep learning models may struggle with complex industrial environments.

Purpose of the Study:

To develop a robust deep learning model for identifying meters and their pointers in low-quality industrial images.
To improve the accuracy and efficiency of pointer recognition in complex scenarios.
To provide a novel approach for pointer detection and pointing prediction.

Main Methods:

Utilized a neural network with an encoder network and jump connections.
Incorporated a modified Convolutional Block Attention Module (CBAM) for keypoint detection.

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Developed an Object Heatmap-Scalarmap Module for pointer tip localization and pointing prediction.

Main Results:

Achieved high recognition correctness with average precision of 0.95 (Object Keypoint Similarity) and 0.763 (Vector Direction Similarity).
Obtained average recall rates of 0.951 and 0.856 on the test set.
Demonstrated superior efficiency and accuracy trade-off compared to other deep learning networks.
Successfully recognized multiple pointer targets, showcasing robustness.

Conclusions:

The proposed deep learning model offers a robust and efficient solution for meter pointer identification in challenging industrial conditions.
The method provides a novel approach for accurate pointer recognition in low-quality images.
The findings support the model's applicability in real-world industrial scenarios.