Power Transmission Line Foreign Object Detection based on Improved YOLOv3 and Deployed to the Chip

With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

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Multi-object Detection by Using CNN for Power Transmission Line Inspection

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Industrial Networks and Intelligent Systems ◽

10.1007/978-3-030-77424-0_28 ◽

2021 ◽

pp. 337-347

Author(s):

Dinh Cong Nguyen ◽

The Cuong Nguyen ◽

Dinh Hung Phan ◽

Nhan Tam Le ◽

Van Vien Tran

Keyword(s):

Object Detection ◽

Transmission Line ◽

Power Transmission ◽

Power Transmission Line

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Electric Equipment Inspection on High Voltage Transmission Line Via Mobile Net-SSD

CONVERTER ◽

10.17762/converter.225 ◽

2021 ◽

pp. 527-540

Author(s):

Wei Zhan, Et al.

Keyword(s):

Deep Learning ◽

Object Detection ◽

Transmission Line ◽

High Voltage ◽

Power Transmission ◽

Electrical Equipment ◽

Power Transmission Line ◽

Single Shot ◽

Learning Methods ◽

Electric Equipment

Daily check and inspection of electrical utilities on the transmission line to find out faults or malfunction data and analyze, it’s to ensure normal state of electrical equipment really difficult in any situation. Machine-controlled inspections by like robots or drones for power transmission infrastructures is an indispensable way to assure the safety of power transmission. Targeted object detection and classification of the power transmission infrastructure is the prerequisite for automatic inspection. In our experiment we have create the dedicated datasets of the electric equipment on power transmission line for multi-object detection, including our data collection, prepossessing and annotation. This work has been done multiple experiments to solve our functional problem and compare novel state of art deep learning methods such as Faster R-CNN, Mask R-CNN, YOLO, and SSD with MobileNet is a base feature extractor, to realize the electric equipment on power transmission line detection. For Condition monitoringand diagnosis identification of the importance of electric equipment on the electric transfer line, in the proposed deep detection approach, the Single-Shot Multi-box Detector (SSD) is a powerful deepmeta-architecture. The results show that our method can automatically detect electric equipment on high voltage transfer defects more accurately and rapidly than lightweight network methods and traditional deep learning methods. Results shed new light on defect detection in actual in progressive scenarios. In our research the main goal to show the implementation of the object detection on electric equipment's inspections on high voltage electric transfer lines on drone video using MobileNet-SSD object detection and recognition.

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