Intelligent Detection of Vegetation Encroachment of Power Lines With Advanced Stereovision

Rong, Shuaiang; He, Lina; Du, Liang; Li, Zuyi; Yu, Shiwen

doi:10.1109/tpwrd.2020.3043433

Cited by 24 publications

(5 citation statements)

References 19 publications

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“…Power system stability and security heavily rely on the timely identification of extraneous objects on transmission lines 1 . T These lines, spanning diverse and challenging terrains such as mountains, urban areas, and construction zones, are particularly prone to damage and present maintenance complexities 2 .…”

Section: Introductionmentioning

confidence: 99%

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Zheng,

Liu,

et al. 2024

Sci Rep

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Detecting foreign objects in power transmission lines is essential for mitigating safety risks and maintaining line stability. Practical detection, however, presents challenges including varied target sizes, intricate backgrounds, and large model weights. To address these issues, this study introduces an innovative GEB-YOLO model, which balances detection performance and quantification. Firstly, the algorithm features a lightweight architecture, achieved by merging the GhostConv network with the advanced YOLOv8 model. This integration considerably lowers computational demands and parameters through streamlined linear operations. Secondly, this paper proposes a novel EC2f mechanism, a groundbreaking feature that bolsters the model’s information extraction capabilities. It enhances the relationship between weights and channels via one-dimensional convolution. Lastly, the BiFPN mechanism is employed to improve the model’s processing efficiency for targets of different sizes, utilizing bidirectional connections and swift feature fusion for normalization. Experimental results indicate the model’s superiority over existing models in precision and mAP, showing improvements of 3.7 and 6.8%, respectively. Crucially, the model’s parameters and FLOPs have been reduced by 10.0 and 7.4%, leading to a model that is both lighter and more efficient. These advancements offer invaluable insights for applying laser technology in detecting foreign objects, contributing significantly to both theory and practice.

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Section: Introductionmentioning

confidence: 99%

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Zheng,

Liu,

et al. 2024

Sci Rep

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“…Davari et al [17] used Faster RCNN to detect defects on distribution lines in each frame of UV-visible video, then identified corona discharges on the lines by color thresholding, and finally described the severity of the occurrence of faults by the ratio of the area of spots to the area of defects. Rong et al [18] applied Faster RCNN, Hough transform, and advanced stereovision (SV) to detect vegetation encroachment on power transmission lines, converting two-dimensional (2D) images of vegetation and power transmission lines into three-dimensional (3D) height and location results to obtain accurate identification and localization. Feng et al [19] proposed a YOLOv5 target detection model based on the method for the automatic detection of insulator defects.…”

Section: Introductionmentioning

confidence: 99%

Research on an Insulator Defect Detection Method Based on Improved YOLOv5

2023

Applied Sciences

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Insulators are widely used in various aspects of the power system and play a crucial role in ensuring the safety and stability of power transmission. Insulator detection is an important measure to guarantee the safety and stability of the transmission system, and accurate localization of insulators is a prerequisite for detection. In this paper, we propose an improved method based on the YOLOv5s model to address the issues of slow localization speed and low accuracy in insulator detection in power systems. In our approach, we first re-cluster the insulator image samples using the k-means algorithm to obtain different sizes of anchor box parameters. Then, we add the non-local attention module (NAM) to the feature extraction module of the YOLOv5s algorithm. The NAM improves the attention mechanism using the weights’ contribution factors and scaling factors. Finally, we recursively replace the ordinary convolution module in the neck network of the YOLOv5 model with the gated normalized convolution (gnConv). Through these improvements, the feature extraction capability of the network is enhanced, and the detection performance of YOLOv5s is improved, resulting in increased accuracy and speed in insulator defect localization. In this paper, we conducted training and evaluation on a publicly available dataset of insulator defects. Experimental results show that the proposed improved YOLOv5s model achieves a 1% improvement in localization accuracy compared to YOLOv5. The proposed method balances accuracy and speed, meeting the requirements of online insulator localization in power system inspection.

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“…The ice thickness is calculated by 3‐D reconstruction, and early warning is generated in the system. In the perspective of application in large‐scale scene, for example, for power line inspection, 3‐D reconstruction capability of BSV is expected to be utilized for precise distance measurement between plants and lines [14].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, due to the advantages of low cost and fast responding time, there is a rising interest in applying binocular stereo vision (BSV) for transmission line inspection [14]. This method uses binocular cameras to reconstruct the 3-D coordinates of the object based on parallax [15,16].…”

Section: Introductionmentioning

confidence: 99%

Intelligent height measurement technology for ground encroachments in large‐scale power transmission corridor based on advanced binocular stereovision algorithms

Tang

Jia

Wang³

et al. 2022

IET Generation Trans & Dist

Self Cite

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To locate the ground encroachments in transmission line corridor effectively, the application of a novel binocular stereo vision (BSV) for distance measurement is realized with improvement and adjustment in calibration and reconstruction process of BSV. Firstly, a calibration method using a 1‐D pole with spatial constraints to replace the 2‐D calibration object is proposed. The field experiment shows that this method improves the accuracy of the calibration in a large‐scale scene by expanding the calibration area. Then, a regulation algorithm to eliminate the difference between focal lengths for binocular cameras is designed to further improve accuracy of calibration. For practical utilization of BSV in the monitor system, the 3‐D reconstructions from world coordinate system (WCS) to ground‐based system is achieved by rotation and transformation of the reconstructed coordinate system and direct height measurement is realized consequently. Further, based on the calibration method being proposed, the partial calibration with a reduced calibration distribution area is studied considering the probable complex terrain. Height measurement calculations are carried out on both hypothetical and real obstacles to verify the validity of the calibration method and postprocessing algorithms. The results demonstrated that the calibration method algorithms successfully realized the practical application of BSV in large‐scale scene with high accuracy. By using this novel BSV technology, the total transmission line inspection cost is expected to be greatly reduced by comparing with manned helicopter and UAV inspections.

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Intelligent Detection of Vegetation Encroachment of Power Lines With Advanced Stereovision

Cited by 24 publications

References 19 publications

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Research on an Insulator Defect Detection Method Based on Improved YOLOv5

Intelligent height measurement technology for ground encroachments in large‐scale power transmission corridor based on advanced binocular stereovision algorithms

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