With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detectio...
Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition me...
As China’s electricity consumption surges, the reliability and safety of long-distance transmission lines become increasingly crucial. Insulators, vital for grid stability, demand accurate defect identification. Existing methods fall short on s...
Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former man...
With the rapid expansion of high-speed rail infrastructure, maintaining the structural integrity of insulators is critical to operational safety. However, conventional defect detection techniques typically rely on extensive labeled datasets, struggle...
With the continuous development and construction of new power systems, using drones to inspect the condition of transmission line insulators has become an inevitable trend. To facilitate the deployment of drone hardware equipment, this paper proposes...
Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed t...
Insulators are critical components of transmission lines, and defective insulators pose a serious threat to the safety of power supply systems. Timely detection of these defects is crucial to prevent catastrophic consequences for human lives and prop...
Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors...
Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of tr...
This paper proposes a lightweight DGNCA-Net insulator defect detection algorithm based on improvements to the YOLOv11 framework, addressing the issues of high computational complexity and low detection accuracy for small targets in machine vision-bas...
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 transmissio...
Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, th...
Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line co...
Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extracti...
To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial im...
To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is pr...
Insulator self-blasts, cracked insulators, and bird nests often lead to large-scale power outages and safety accidents, while the detection system based on a single UAV and YOLOv7 is difficult to meet the speed and accuracy requirements in actual det...
Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning)...
To address the challenges of high model complexity and low accuracy in detecting small targets in insulator defect detection using UAV aerial imagery, we propose a lightweight algorithm, PAL-YOLOv8. Firstly, the baseline model, YOLOv8n, is enhanced b...
The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which...
Detecting defects in transmission line insulators is crucial to prevent power grid failures as power systems continue to expand. This study introduces YOL011n-SSA, an enhanced insulator defect detection technique method that addresses the challenges...
Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network...
Insulators serve as critical insulating components in power transmission lines, and their defects are one of the primary causes of power outages in power grids. Power companies widely utilize unmanned aerial vehicle (UAV) inspections to collect image...
In modern power systems, drones are increasingly being utilized to monitor the condition of critical power equipment. However, limited computing capacity is a key factor limiting the application of drones. To optimize the computational load on drones...
For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First,...
The safety of power transmission lines is crucial to public well-being, with insulators being prone to failures such as self-detonation. However, images captured by unmanned aerial vehicles (UAVs) carrying optical sensors often face challenges, inclu...
Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in...
As a principal insulating component in power transmission systems, the integrity of the insulator is of paramount importance for ensuring the safe and reliable operation of transmission lines. While the deployment of aerial photography technology has...
Real-time insulator defect detection is critical for ensuring the reliability and safety of power transmission systems. However, deploying deep learning models on edge devices presents significant challenges due to limited computational resources and...
In the detection of small targets such as insulator defects and flashovers, the existing YOLOv11 has problems such as insufficient feature extraction and difficulty in balancing model lightweight and detection accuracy. We propose a lightweight archi...
To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception...
Against the background of the “dual carbon” goal and the construction of a new power system, the intelligent operation and maintenance of insulators for ultra-high voltage transmission lines face challenges such as difficulty in detecting...
This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firs...
Unmanned aerial vehicle (UAV) inspection has become the mainstream of transmission line inspection, and the detection of insulator defects is an important part of UAV inspection. On the premise of ensuring high accuracy and detection speed, an improv...
The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms...
During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings...
Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial im...
Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from c...
Insulators are essential for electrical insulation and structural support in transmission lines. With the advancement of deep learning, object detection algorithms have become primary tools for detecting insulator defects. However, challenges such as...
Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power...
By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the...
Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect d...
To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model...
In the field of industry, defect detection based on YOLO models is widely used. In real detection, the method of defect detection of insulative baffles is artificial detection. The work efficiency of this method, however, is low because the detection...
To address the challenges of balancing accuracy and speed, as well as the parameters and FLOPs in current insulator defect detection, we propose an enhanced insulator defect detection algorithm, ML-YOLOv5, based on the YOLOv5 network. The backbone mo...
To solve the problem of scale variation in insulator images captured by drones, caused by the lack of control over angle and distance, which makes it hard to detect subtle defects, this paper proposes an instance segmentation method based on an impro...
To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL...
This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firs...
To further improve the accuracy and speed of UAV inspection of transmission line insulator defects, this paper proposes an insulator detection and defect identification algorithm based on YOLOv4, which is called DSMH-YOLOv4. In the feature extraction...
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