# Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

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## Abstract

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## 1. Introduction

## 2. System and Workflow

#### 2.1. UAV-Magnetometer System

#### 2.2. Workflow for UAV-Borne Magnetic Survey

- Background field elimination: The background field consists of the local geomagnetic field and the magnetic field produced by the ambient sources, like power grid, traffic, buildings. We are concerned with the magnetic anomaly signal from the underground objects, so the background field should firstly be removed.
- UAV interference field removal: Unlike the interference field from the external environment, UAV interference field is an inherent noise signal from system, which is related to the attitude of the drone. Herein, we propose a calibration method based on the signal correlation to separate the magnetic anomaly signal from the total field signal.
- Data gridding: It is customary to perform data gridding, which is to compute the magnetic field of regular grid nodes from the irregularly distributed sampling points by interpolation. Thus, a two-dimensional contour magnetic map is produced to reflect the abnormal distribution of the entire test area.

## 3. Materials and Methods

#### 3.1. Background Field Elimination

#### 3.2. UAV Interference Field Removal

#### 3.3. Data Gridding

#### 3.4. YOLOv3-based Euler Deconvolution

#### 3.4.1. Euler Deconvolution

_{0}, y

_{0}, z

_{0}) denotes the location of anomaly source, (x, y, z) denotes the location of observation point, $T$,$\partial T/\partial x$,$\partial T/\partial y$,$\partial T/\partial z$ denote the anomalous field and its gradient fields in -x, -y, -z direction. N is the structure of index (SI), characterizing the attenuation rate of the amplitude of the anomalous field with the distance, which depends on the type of the sources. For the detection of near-surface targets, the anomaly source such as UXO is approximated to a point-like dipole, the SI N is generally between 2.5 and 3 [24,25,26,27].

#### 3.4.2. YOLOv3

- A window contains at least three observation points because there are three unknown parameters in Equation (7).
- Based on the principal of Euler deconvolution, the anomaly data within the windows must be generated from an isolated object, avoiding the effects from the adjacent sources.
- For the real data, the higher SNR of magnetic data is, the more reliable the results of Euler deconvolution are. The size of window is determined by a threshold that is defined as the ratio of the maximum and minimum values of the magnetic field in a window. The anomaly field with lower SNR is removed by the setting of threshold, which is set to 50% here.

#### 3.4.3. Summary of YOLOv3-based Euler Deconvolution

Algorithm 1: YOLOv3-based Euler Deconvolution |

Input: Gridded magnetic data and a 2D magnetic contour map Output: The location of underground targets1: The magnetic contour map is inputted into the trained YOLOv3 2: A total of N _{windows} anomaly windows are detected by YOLOv3, extract the magnetic field data inside each window3: for k = 1:N_{windows} do4: Determine the Euler solution ${X}_{euler}^{k}$ based on the data within the window 5: end for6: Cluster, if ${X}_{euler}^{i}-{X}_{euler}^{j}<30\text{}\mathrm{cm}$ (${X}_{euler}^{i}$ and ${X}_{euler}^{j}$ are Euler solutions obtained by Positive and Negative anomaly windows). 7: return the location of potential targets, which is same as the clustered results |

## 4. Field Experiment

#### 4.1. UAV-Borne Magnetic Survey

#### 4.2. Results

## 5. Discussion

#### 5.1. Analysis on the Processing Flow

#### 5.2. Limits of the Research and the Future Work

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The Unmanned Aerial Vehicle (UAV)-magnetometer system, carrying two magnetometers, radar altimeter, GPS, data recording and power module. The top magnetic sensor is labelled as magnetometer 1 and the bottom magnetic sensor is labelled as magnetometer 2.

**Figure 3.**The elimination of background field. (

**a**) The raw magnetic field along the profile; (

**b**) The filtered magnetic field by a low-pass filter with ${f}_{c}$ = 3 Hz. (

**c**) The detrended magnetic field.

**Figure 5.**The removal of the UAV interference field. (

**a**) The outputs of magnetometer 1; (

**b**) The outputs of magnetometer 2; (

**c**) The separated UAV interference field; (

**d**) The separated magnetic anomaly field.

**Figure 6.**Partial samples and labelled anomalies in the dataset of You Only Look Once version 3 (YOLOv3). Each image is a 2D magnetic contour map, the unit of magnetic field in all figures is nano Tesla (nT), the targets to be detected are divided into two types: Positive and Negative. (

**a**), (

**b**), and (

**c**) show three magnetic contour maps generated by the same target with different azimuths and declinations, remanence, and locations. (

**d**), (

**e**) and (

**f**) show three magnetic contour maps generated by different numbers and sizes of targets, background magnetic fields.

**Figure 7.**The workflow of sliding window (SW) Euler deconvolution [24], the size of windows varies from 3 to 25 grid points, and the “Filter” means filtering out the Euler solutions whose structure index $N$ is less than the threshold 2.2.

**Figure 8.**The planning of test site and flight path, multiple parallel profiles along the south-north direction cover the test area. The “START” is the starting point of the drone in the experiment and the trimmed profiles in the local coordinate are given on the right.

**Figure 9.**The magnetic map of raw magnetic field. (

**a**) Magnetometer 1; (

**b**) Magnetometer 2. (The unit of magnetic field in all figures is nano Tesla (nT)).

**Figure 10.**The magnetic map with the background magnetic field removed. (

**a**) The filtered magnetic data from magnetometer 1; (

**b**) The filtered magnetic data from magnetometer 2. (

**c**) The detrended magnetic data from magnetometer 1; (

**d**) The detrended magnetic data from magnetometer 2.

**Figure 13.**The estimated positions of two methods, white round marker ‘○’ is the real location of buried targets measured by the Real-Time Kinematic (RTK) in advance, black cross ‘+’ marker is the estimated location. (

**a**) The YOLOv3-based Euler Deconvolution; (

**b**) The SW Euler Deconvolution.

**Figure 14.**The positioning error of two methods. (

**a**) The YOLOv3-based Euler Deconvolution; (

**b**) The SW-based Euler Deconvolution.

True Location (m) | YOLOv3-based Euler Deconvolution (m) | SW-based Euler Deconvolution (m) | |
---|---|---|---|

1 | (6.17, 33.96, 0.8) | (6.03, 34.19, 0.70) | (5.98, 34.20, 0.11) |

2 | (6.31, 26.61, 1.0) | (6.32, 26.52, 0.75) | (6.45, 26.52, 0.21) |

3 | (3.99, 22.15, 0.1) | (4.01, 22.24, 0.10) | (3.85, 22.22, -0.40) |

4 | (17.16, 29.47, 1.1) | (16.95, 29.54, 1.06) | (16.72, 29.43, 0.11) |

5 | (18.07, 22.21, 0.5) | (17.78, 22.18, 0.56) | (17.96, 22.18, -0.35) |

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**MDPI and ACS Style**

Mu, Y.; Zhang, X.; Xie, W.; Zheng, Y.
Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey. *Remote Sens.* **2020**, *12*, 452.
https://doi.org/10.3390/rs12030452

**AMA Style**

Mu Y, Zhang X, Xie W, Zheng Y.
Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey. *Remote Sensing*. 2020; 12(3):452.
https://doi.org/10.3390/rs12030452

**Chicago/Turabian Style**

Mu, Yaxin, Xiaojuan Zhang, Wupeng Xie, and Yaoxin Zheng.
2020. "Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey" *Remote Sensing* 12, no. 3: 452.
https://doi.org/10.3390/rs12030452