# Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Belt Conveyor Maintenance

#### 1.2. Laser Scanning

#### 1.3. Motivation

- extracting only point representing the belt surface from the full point cloud of the surroundings,
- detecting and evaluating local damage to the belt surface,
- identifying belt edges defects and analysing edge straightness.

## 2. Materials and Methods

#### 2.1. Methodology of Conveyor Belt Geometry Measurement

#### 2.1.1. Data Acquisition

#### 2.1.2. Point Cloud Data Pre-Processing

#### 2.1.3. Point Cloud Supervised Classification and Segmentation

#### 2.2. Belt Geometry Condition Monitoring

#### 2.2.1. Belt Surface Damage Detection

#### 2.2.2. Belt Edges Condition Evaluation

#### 2.3. Test Environment

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 8.**Riegl VZ-400i TLS [61].

**Figure 18.**Segmented points representing belt longitudinal edges (in blue and green) and noise (in red).

**Table 1.**3D shape features extracted from the neighborhood geometry [49].

Local 3D Shape Descriptor | Definition |
---|---|

sum of eigenvalues | ${\sum}_{\lambda}={\sum}_{i=1}^{3}{\lambda}_{i}$ |

planarity | ${P}_{\lambda}=\frac{{\lambda}_{2}-{\lambda}_{3}}{{\lambda}_{1}}$ |

linearity | ${L}_{\lambda}=\frac{{\lambda}_{1}-{\lambda}_{2}}{{\lambda}_{1}}$ |

anisotropy | ${A}_{\lambda}=\frac{{\lambda}_{1}-{\lambda}_{3}}{{\lambda}_{1}}$ |

omnivariance | ${O}_{\lambda}=\sqrt[3]{{\lambda}_{1}{\lambda}_{2}{\lambda}_{3}}$ |

eigenentropy | ${E}_{\lambda}=-{\sum}_{i=1}^{3}{\lambda}_{i}ln{\lambda}_{i}$ |

first principal component | $P{C}_{1,\lambda}=\frac{{\lambda}_{1}}{{\sum}_{\lambda}}$ |

second principal component | $P{C}_{2,\lambda}=\frac{{\lambda}_{2}}{{\sum}_{\lambda}}$ |

third principal component (curvature) | ${C}_{\lambda}=\frac{{\lambda}_{3}}{{\sum}_{\lambda}}$ |

verticality | ${V}_{\lambda}=1-\left|{n}_{z}\right|$ |

Class | Precision | Recall | ${\mathit{F}}_{1}$ |
---|---|---|---|

belt surface points | 0.992 | 0.983 | 0.988 |

other points | 0.999 | 1.000 | 1.000 |

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**MDPI and ACS Style**

Trybała, P.; Blachowski, J.; Błażej, R.; Zimroz, R.
Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface. *Remote Sens.* **2021**, *13*, 55.
https://doi.org/10.3390/rs13010055

**AMA Style**

Trybała P, Blachowski J, Błażej R, Zimroz R.
Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface. *Remote Sensing*. 2021; 13(1):55.
https://doi.org/10.3390/rs13010055

**Chicago/Turabian Style**

Trybała, Paweł, Jan Blachowski, Ryszard Błażej, and Radosław Zimroz.
2021. "Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface" *Remote Sensing* 13, no. 1: 55.
https://doi.org/10.3390/rs13010055