This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines
by
Xiang Zhang
Xiang Zhang 1,
Jianwei Zhang
Jianwei Zhang 2 and
Xiaoqiang Jia
Xiaoqiang Jia 3,*
1
School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China
2
School of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, China
3
School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 7469; https://doi.org/10.3390/app14177469 (registering DOI)
Submission received: 15 July 2024
/
Revised: 16 August 2024
/
Accepted: 17 August 2024
/
Published: 23 August 2024
Abstract
Power transmission lines frequently face threats from lightning strikes, severe storms, and chemical corrosion, which can lead to damage in steel–aluminum-stranded wires, thereby seriously affecting the stability of the power system. Currently, manual inspections are relatively inefficient and high risk, while drone inspections are often limited by complex environments and obstacles. Existing detection algorithms still face difficulties in identifying broken strands. To address these issues, this paper proposes a new method called SL-YOLOv8. This method incorporates an improved You Only Look Once version 8 (YOLOv8) algorithm, specifically designed for online intelligent inspection robots to detect broken strands in transmission lines. Transmission lines are susceptible to lightning strikes, storms, and chemical corrosion, which is leading to the potential failure of steel- and aluminum-stranded lines, and significantly impacting the stability of the power system. Currently, manual inspections come with relatively low efficiency and high risk, and Unmanned Aerial Vehicle (UAV) inspections are hindered by complex situations and obstacles, with current algorithms making it difficult to detect the broken strand lines. This paper proposes SL-YOLOv8, which is a broken transmission line strand detection method for an online intelligent inspection robot combined with an improved You Only Look Once version 8 (YOLOv8). By incorporating the Squeeze-and-Excitation Network version 2 (SENet_v2) into the feature fusion network, the method effectively enhances adaptive feature representation by focusing on and amplifying key information, thereby improving the network’s capability to detect small objects. Additionally, the introduction of the LSKblockAttention module, which combines Large Selective Kernels (LSKs) and the attention mechanism, allows the model to dynamically select and enhance critical features, significantly enhancing detection accuracy and robustness while maintaining model precision. Compared with the original YOLOv8 algorithm, SL-YOLOv8 demonstrates improved precision recognition accuracy in Break-ID-1632 and cable damage datasets. The precision is increased by 3.9% and 2.7%, and the recall is increased by 12.2% and 2.3%, respectively, for the two datasets. The mean average precision (mAP) at the Intersection over Union (IoU) threshold of 0.5 is also increased by 4.9% and 1.2%, showing the SL-YOLOv8’s effectiveness in accurately identifying small objects in complex situations.
Share and Cite
MDPI and ACS Style
Zhang, X.; Zhang, J.; Jia, X.
An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines. Appl. Sci. 2024, 14, 7469.
https://doi.org/10.3390/app14177469
AMA Style
Zhang X, Zhang J, Jia X.
An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines. Applied Sciences. 2024; 14(17):7469.
https://doi.org/10.3390/app14177469
Chicago/Turabian Style
Zhang, Xiang, Jianwei Zhang, and Xiaoqiang Jia.
2024. "An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines" Applied Sciences 14, no. 17: 7469.
https://doi.org/10.3390/app14177469
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.