# US 12,183,485 B2 · Method and system for detecting typical object of transmission line based on unmanned aerial vehicle (UAV) federated learning

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- Number: US 12,183,485 B2
- Document ID: US12183485B2
- Status: Granted
- Kind: B2
- Application number: 17/987,203
- Filed: 2022-11-15
- Published: 2023-07-27
- Granted: 2024-12-31
- Earliest claimed: 2022-01-26
- Assignees: North China Electric Power University
- Applicants: North China Electric Power University
- Inventors: Xin Wu; Tanxin Pi; Yawen Yu
- Web page: https://patentgrep.com/patent/US12183485B2

## CPC

- H01B 7/32: with arrangements for indicating defects, e.g. breaks or leaks
- B64C 39/024: {of the remote controlled vehicle type, i.e. RPV}
- G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes}
- G06V 10/70: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88)
- G06V 10/82: using neural networks
- G06V 20/17: taken from planes or by drones
- G06V 20/176: {Urban or other man-made structures}
- B64U 2101/30: for imaging, photography or videography
- G06V 2201/07: Target detection
- Y04S 10/50: Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- H01B: CABLES; CONDUCTORS; INSULATORS; SELECTION OF MATERIALS FOR THEIR CONDUCTIVE, INSULATING OR DIELECTRIC PROPERTIES (selection for magnetic properties H01F1/00; waveguides H01P {; printed circuits H05K})
- B64C: AEROPLANES; HELICOPTERS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.

## Claims

### Claim 1

A method for detecting a typical object of a transmission line based on unmanned aerial vehicle (UAV) federated learning, comprising:
determining a detection model for a typical object of a transmission line by using a you only look once, version 3 (YOLOv3) object detection algorithm according to a prior database for the typical object of the transmission line, wherein the prior database for the typical object of the transmission line comprises a plurality of pictures of the typical object of the transmission line and a tag corresponding to each of the plurality of pictures, and the typical object of the transmission line comprises an insulator, a wire, or a pin;
dividing a UAV network into multiple federated learning units, and acquiring the plurality of pictures, taken by the UAV network, of the typical object of the transmission line and the tag corresponding to each of the plurality of pictures to determine a training database;
training, based on a Horovod framework and a FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object of the transmission line, and determining the trained UAV network according to the trained federated learning unit; and
determining, by the trained UAV network, the typical object in each of the plurality of pictures of the typical object of the transmission line.

### Claim 2 (depends on 1)

The method for detecting the typical object of the transmission line based on UAV federated learning according to claim 1, wherein training, based on the Horovod framework and the FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object of the transmission line and determining the trained UAV network according to the trained federated learning unit specifically comprises:
distributing, based on the Horovod framework, parallel computing power of each federated learning unit according to the training database and the detection model for the typical object of the transmission line to determine a weight of each federated learning unit;
aggregating, based on the FATE federated learning framework, the weights of all the federated learning units; and
transferring the aggregated weights to each federated learning unit, and returning to the step of distributing, based on the Horovod framework, parallel computing power of each federated learning unit according to the training database and the detection model for the typical object of the transmission line to determine a weight of each federated learning unit until a Loss error function converges; and determining the trained UAV network.

### Claim 3 (depends on 2)

The method for detecting the typical object of the transmission line based on UAV federated learning according to claim 2, wherein the error function Loss is:
Loss=λcoordL1+λcoordL2+L3+L4;
wherein, L1 represents a center coordinate error; L2 represents a width-height coordinate error; L3 represents a confidence error; L4 represents a classification error; and λcoord represents a joint error coefficient.

### Claim 4

A system for detecting a typical object of a transmission line based on UAV federated learning, comprising:
a detection model determining module for a typical object of a transmission line, configured to determine the detection model for the typical object of the transmission line by using a you only look once, version 3 (YOLOv3) object detection algorithm according to a prior database for the typical object of the transmission line, wherein the prior database for the typical object of the transmission line comprises a plurality of pictures of the typical object of the transmission line and a tag corresponding to each of the plurality of pictures, and the typical object of the transmission line comprises an insulator, a wire, or a pin;
a federated learning unit and training database determining module, configured to divide a UAV network into multiple federated learning units and determine the training database by acquiring the plurality of pictures, taken by the UAV network, of the typical object of the transmission line and the tag corresponding to each of the plurality of pictures;
a trained UAV network determining module, configured to train, based on a Horovod framework and a FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object of the transmission line, and determine the trained UAV network according to the trained federated learning units; and
a detecting module, configured to determine, by the trained UAV network, the typical object in each of the plurality of pictures of the typical object of the transmission line.

### Claim 5 (depends on 4)

The system for detecting the typical object of the transmission line based on UAV federated learning according to claim 4, wherein the trained UAV network determining module specifically comprises:
a weight training unit for the federated learning unit, configured to distribute, based on the Horovod framework, parallel computing power of each federated learning unit according to the training database and the detection model for the typical object of the transmission line to determine a weight of each federated learning unit;
a weight aggregating unit for the federated learning unit, configured to aggregate, based on the FATE federated learning framework, the weights of all the federated learning units; and
an iterating unit, configured to transfer the aggregated weights to each federated learning unit and return to the weight training unit of the federated learning unit.

### Claim 6 (depends on 5)

The system for detecting the typical object of the transmission line based on UAV federated learning according to claim 5, wherein a error function Loss is:
Loss=ΔcoordL1+λcoordL2+L3+L4;
wherein, L1 represents a center coordinate error; L2 represents a width-height coordinate error; L3 represents a confidence error; L4 represents a classification error; and λcoord represents a joint error coefficient.