# A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. AMR Detector

#### 2.1.1. Localization of the Objects

#### 2.1.2. Resizing Cropped Images

#### 2.1.3. Regression of the Grid of Reference Points

#### 2.1.4. Characteristics of the Network

#### 2.2. Preparation of Training Data

#### 2.2.1. Labeling and Preparation of the Dataset

#### 2.2.2. Localization of the Centers of the Devices

#### 2.2.3. Enumeration of the Symbols

#### 2.3. Experiments

#### 2.3.1. Type of Device Detection

#### 2.3.2. Symbols’ Coordinates Detection

## 3. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Reference points (blue) for the class N (not shown in the image), the diameter d of the circle built on top of symbol’s reference points (RP) (not shown in the image) and the center $\{{x}_{0},{y}_{0}\}$ of an analog dial gauge (bold green dot).

**Figure 7.**Examples of grid generation for various devices. Detection takes around 3ms of GPU computations for a {1024 × 512} image. The bottom row demonstrates some of the most difficult cases.

Layer Type | Window/Step/Output | Kernel | Number of Parameters |
---|---|---|---|

Conv1 | $2\times 2$ / $2\times 2$ / $LRelu$ | $4\times 4\times 4$ | 125 |

Conv2 | $2\times 2$ / $2\times 2$ / $LRelu$ | $8\times 8\times 8$ | 729 |

Conv3 | $2\times 2$ / $2\times 2$ / $LRelu$ | $16\times 16\times 16$ | 4913 |

Conv4 | $2\times 2$ / $2\times 2$ / $LRelu$ | $32\times 32\times 32$ | 36 K |

Conv5 | $2\times 2$ / $2\times 2$ / $LRelu$ | $64\times 64\times 64$ | 275 K |

Conv6 | $2\times 2$ / $2\times 2$ / $LRelu$ | $128\times 128\times 128$ | 2.1 M |

Conv7 | $2\times 2$ / $2\times 2$ / $LRelu$ | $256\times 256\times 256$ | 17 M |

Conv8 | $2\times 2$ / $2\times 2$ / $LRelu$ | $512\times 512\times 512$ | 135 M |

Conv9 | $2\times 2$ / $1\times 1$ / $LRelu$ | $512\times 512\times 1536$ | 406 M |

Total | − | − | 560 M |

Layer Type | Window / Step / Output | Kernel | Number of Parameters |
---|---|---|---|

Conv1 | $2\times 2$ / $2\times 2$ / $LRelu$ | $4\times 4\times 4$ | 125 |

Conv2 | $2\times 2$ / $2\times 2$ / $LRelu$ | $16\times 16\times 16$ | 4913 |

Conv3 | $2\times 2$ / $2\times 2$ / $LRelu$ | $64\times 64\times 64$ | 275 K |

Conv4 | $2\times 2$ / $2\times 2$ / $LRelu$ | $256\times 256\times 256$ | 17 M |

Conv5 | $2\times 2$ / $2\times 2$ / $LRelu$ | $256\times 256\times 256$ | 17 M |

Conv6 | $2\times 2$ / $2\times 2$ / $LRelu$ | $256\times 256\times 256$ | 17 M |

Conv7 | $2\times 2$ / $2\times 2$ / $LRelu$ | $256\times 256\times 256$ | 17 M |

Conv8 | $2\times 2$ / $1\times 1$ / $LRelu$ | $256\times 256\times 18$ | 1.2 M |

Total | − | − | 70 M |

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**MDPI and ACS Style**

Alexeev, A.; Kukharev, G.; Matveev, Y.; Matveev, A. A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions. *Mathematics* **2020**, *8*, 1104.
https://doi.org/10.3390/math8071104

**AMA Style**

Alexeev A, Kukharev G, Matveev Y, Matveev A. A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions. *Mathematics*. 2020; 8(7):1104.
https://doi.org/10.3390/math8071104

**Chicago/Turabian Style**

Alexeev, Alexey, Georgy Kukharev, Yuri Matveev, and Anton Matveev. 2020. "A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions" *Mathematics* 8, no. 7: 1104.
https://doi.org/10.3390/math8071104