A Novel Defect Detection Method for Overhead Ground Wire

Xiao, Yao; Xiong, Lan; Zhang, Zhanlong; Dan, Yihua

doi:10.3390/s24010192

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the swift and precise real-time detection of surface defects in WR represents a pressing requirement in industrial production. Xiao et al [3] designed a defect detection device according to the magnetic leakage characteristics around the overhead ground wire. According to the influence of different defects on MFL signal, the defects are accurately located and quantified.…”

Section: Introductionmentioning

confidence: 99%

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Zhenbin,

Meishuai,

Xun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The presence of surface defects in wire ropes (WR) may lead to potential safety hazards and performance degradation, necessitating timely detection and repair. Hence, this paper proposes a method for detecting surface defects in WR based on the deep learning models YOLOv8s and U-Net, aiming to identify surface defects in real-time and extract defect data, thereby enhancing the efficiency of surface defect detection. Firstly, the ECA attention mechanism is incorporated into the YOLOv8 algorithm to enhance detection performance, achieving real-time localization and identification of surface defects in WR. Secondly, in order to obtain detailed defect data, the U-Net semantic segmentation algorithm is employed for morphological segmentation of defects, thereby obtaining the contour features of surface defects. Finally, in conjunction with OpenCV technology, the segmentation results of the defects are quantified to extract data, obtaining parameters such as the area and perimeter of the surface defects in the WR. Experimental results demonstrate that the improved YOLOv8-ECA model exhibits good accuracy and robustness, with the model’s mAP@0.5 reaching 84.78%, an increase of 1.13% compared to the base model, an accuracy rate of 90.70%, and an FPS of 65. The U-Net model can efficiently perform segmentation processing on surface defects of WR, with an mIOU of 83.54% and an mPA of 90.78%. This method can rapidly, accurately, and specifically detect surface defects in WR, which is of significant importance in preventing industrial production safety accidents.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Zhenbin,

Meishuai,

Xun

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network

Gou,

Tang,

Song

et al. 2024

Processes

View full text Add to dashboard Cite

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples’ diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines.

show abstract

A Novel Defect Detection Method for Overhead Ground Wire

Cited by 2 publications

References 26 publications

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network

Contact Info

Product

Resources

About