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Research on improved YOLOV7-SSWD digital meter reading recognition algorithms.

Zhenguan Cao1, Haixia Yang1, Liao Fang1

  • 1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China.

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Summary

This study introduces the YOLOV7-SSWD model for improved meter reading recognition in inspection robots. The novel approach enhances detection accuracy and localization, crucial for automated tasks.

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Meter reading recognition is vital for robotic inspection tasks.
  • Existing algorithms suffer from low detection accuracy and inaccurate localization.
  • Need for robust and precise meter reading recognition systems.

Purpose of the Study:

  • To propose a novel detection model, YOLOV7-SSWD, to enhance meter reading recognition accuracy and localization.
  • To improve the performance of the YOLOV7-Tiny model using a multi-head attention mechanism.
  • To provide a reliable solution for automated meter reading by inspection robots.

Main Methods:

  • Developed YOLOV7-SSWD by improving the YOLOV7-Tiny model.
  • Incorporated Wise-IoU loss function to address sample quality imbalance.
  • Introduced SiLU activation function for enhanced generalization and SimAM for improved feature extraction.
  • Implemented a dynamic detection header for multi-scale feature fusion.

Main Results:

  • The YOLOV7-SSWD model achieved a mean Average Precision (mAP) of 89.8% and an F1-score of 0.84.
  • Demonstrated an 8.1% increase in mAP and a 0.1 increase in F1-score compared to the original network.
  • Ablation experiments validated the effectiveness of each proposed component.

Conclusions:

  • The YOLOV7-SSWD algorithm significantly improves localization and recognition accuracy for meter reading.
  • The proposed model offers a valuable reference for deploying inspection robots for automated meter reading.
  • Enhanced performance contributes to more reliable and efficient robotic inspection systems.