Line Inspection Research Articles

TLI-YOLOv5: A Lightweight Object Detection Framework for Transmission Line Inspection by Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) have become an important tool for transmission line inspection, and the inspection images taken by UAVs often contain complex backgrounds and many types of targets, which poses many challenges to object detection algorithms. In this paper, we propose a lightweight object detection framework, TLI-YOLOv5, for transmission line inspection tasks. Firstly, we incorporate the parameter-free attention module SimAM into the YOLOv5 network. This integration enhances the network’s feature extraction capabilities, without introducing additional parameters. Secondly, we introduce the Wise-IoU (WIoU) loss function to evaluate the quality of anchor boxes and allocate various gradient gains to them, aiming to improve network performance and generalization capabilities. Furthermore, we employ transfer learning and cosine learning rate decay to further enhance the model’s performance. The experimental evaluations performed on our UAV transmission line inspection dataset reveal that, in comparison to the original YOLOv5n, TLI-YOLOv5 increases precision by 0.40%, recall by 4.01%, F1 score by 1.69%, mean average precision at 50% IoU (mAP50) by 2.91%, and mean average precision from 50% to 95% IoU (mAP50-95) by 0.74%, while maintaining a recognition speed of 76.1 frames per second and model size of only 4.15 MB, exhibiting attributes such as small size, high speed, and ease of deployment. With these advantages, TLI-YOLOv5 proves more adept at meeting the requirements of modern, large-scale transmission line inspection operations, providing a reliable, efficient solution for such demanding tasks.

Benchmarking Generations of You Only Look Once Architectures for Detection of Defective and Normal Long Rod Insulators

Effective infrastructure monitoring is a priority in all technical fields in this century. In high-voltage transmission networks, line inspection is one such task. Fault detection of insulators is crucial, and object detection algorithms can handle this problem. This work presents a comparison of You Only Look Once architectures. The different subtypes of the last three generations (v3, v4, and v5) are compared in terms of losses, precision, recall, and mean average precision on an open-source, augmented dataset of normal and defective insulators from the State Grid Corporation of China. The primary focus of this work is a comprehensive subtype analysis, providing a useful resource for academics and industry professionals involved in insulator detection and surveillance projects. This study aims to enhance the monitoring of insulator health and maintenance for industries relying on power grid stability. YOLOv5 subtypes are found to be the most suitable for this computer vision task, considering their mean average precision, which ranges between 98.1 and 99.0%, and a frame per second rate between 27.1 and 212.8, depending on the architecture size. While their predecessors are faster, they are less accurate. It is also discovered that, for all generations, normal-sized and large architectures generally demonstrate better accuracy. However, small architectures are noted for their significantly faster processing speeds.

Deep-Learning-Based Detection of Transmission Line Insulators

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications.

Rate-accuracy optimized quantization algorithm based on ROI image coding in power line inspection

Rate-accuracy optimized quantization algorithm based on ROI image coding in power line inspection

Measuring defects in high-speed production lines—a three-phase convolutional neural network model

Conventional automatic optical inspection (AOI) systems using rule-based image processing suffer from precision and velocity issues, particularly when simultaneously measuring multiple defects of a product in a high-speed production line. Such AOI stations usually become a bottleneck in the line. This paper presents a three-phase model for defect detection based on convolutional neural network to release the cycle time of the line. The phase I model using a deep residual network (ResNet50) performs defect classification of products with high accuracy. The phase II model is another independent ResNet50 that classifies defect products into defect categories, rescuing good products that have been mistakenly killed in the previous stage and replacing the conventional re-inspection labors. The phase III model is a you only look once—based network that detects multiple defects and their positions simultaneously in a single product, providing informative quality data for continuous improvement. The proposed model successfully resolves the issue of multiple-defect and multiple-len quality inspection in a high-speed production line. The proposed model resolved defect inspection by integrating object detection and defect classification simultaneously. By deploying the three-phase model in a tiny electronic connector component production line, the present model has demonstrated that it reaches high precision and facilitates prompt quality correction for high-speed production lines.

Research and Application of the Obstacle Avoidance System for High-Speed Railway Tunnel Lining Inspection Train Based on Integrated 3D LiDAR and 2D Camera Machine Vision Technology

This study presents an innovative, intelligent obstacle avoidance module intended to significantly enhance the collision prevention capabilities of the robotic arm mechanism onboard a high-speed rail tunnel lining inspection train. The proposed module employs a fusion of ORB-SLAM3 and Normal Distribution Transform (NDT) point cloud registration techniques to achieve real-time point cloud densification, ensuring reliable detection of small-volume targets. By leveraging spatial filtering, cluster computation, and feature extraction, precise obstacle localization information is further obtained. A fusion of multi-modal data is achieved by jointly calibrating 3D LiDAR and camera images. Upon validation through field testing, it is demonstrated that the module can effectively detect obstacles with a minimum diameter of 0.5 cm, with an average deviation controlled within a 1–2 cm range and a safety margin of 3 cm, effectively preventing collisions. Compared to traditional obstacle avoidance sensors, this module provides information across more dimensions, offering robust support for the construction of powerful automated tunnel inspection control systems and digital twin lifecycle analysis techniques for railway tunnels.

Comparison of methods for monitoring icing of high-voltage power lines and an overview of measuring equipment used to diagnose such lines

The low structural reliability of overhead lines, due to continuous climatic influence, becomes the cause of electrical network accidents and unreliable power supply. Ensuring the trouble-free operation of overhead lines is a complex task, the solution of which is of the utmost importance for the normal functioning of the entire infrastructure. In the autumn-winter period, difficult weather conditions almost completely exclude the quick completion of repair works. A fairly large share of accidents occurs during the formation of icy deposits on wires and lightning protection cables. Today, visual inspections of high-voltage power lines remain the main way of diagnosing icy deposits. In practice, it is necessary to equip overhead lines with ice load telemetry devices, which will allow monitoring the formation of ice deposits on wires in real time. The following diagnostic methods are used in modern ice monitoring devices: strainometric, fiber-optic, aerodynamic, locational, and instrumental-parametric. The tensiometric method of diagnosis is implemented by means of direct measurement of the icy load on the wire with subsequent comparison of the measured values with predetermined values of the threshold loads. To implement this diagnostic method, magnetoelastic force measuring sensors are used. Such a sensor is connected between the traverse of the U-shaped support and the upper end of the corresponding garland of insulators with a phase wire. Ice, wind and ice-wind loads are measured separately. This method has a limited scope of application, namely for diagnosing the formation of ice on the phase wires of intermediate spans of single-circuit lines with two-post U-shaped resistances. The fiber-optic method of strain gauge measurements is implemented in object control systems under difficult operating conditions, at nuclear power plants. The mechanical effort sensor (fiber-optic strain sensor) implements the fiber-optic method of strain measurement. Fiber-optic strain sensors are characterized by high accuracy due to resistance to interference and complexity of execution.

Luminescent diagnostics and quantitative assessment of malaria based on a lateral flow immunoassay with cdte quantum dots

A lateral flow immunoassay (LFI) is a simple, low-cost and rapid diagnostic tool to identify various diseases by detecting analytes such as antibodies, parasites, or other relevant biomarkers. However, most LFIs can only confirm the presence or absence of a target analyte being applied as qualitative diagnostic tools. In addtion, the conventional LFIs, which require visual inspection of the test line, may demonstrate insufficient sensitivity for the mild form or early detection of infections and for this reason, the result can be falsely negative. This study presents the results of employing thioglycolic acid capped CdTe quantum dots as a probe to enhance luminescence and subsequently detection sensitivity in LFIs for diagnosis of malaria. The chemical route synthesis of thioglycolic acid capped CdTe quantum dots was optimized systematically by probing optical properties of the quantum dots. These optimized quantum dots of thioglycolic acid capped CdTe have been conjugated with the anti-malaria antibodies against HRP2 protein (P. falciparum) and were then incorporated into LFIs. Further, an image processing code has been developed to carry out the quantification of malaria parasites in terms of the ratio on intensities of control and test lines on the LFIs. The results have been compared with those obtained using the standard, colloidal gold based LFIs. It has been demonstrated that sensitivity and lower detection limits of malaria under low parasite concentration increase significantly due to enhanced luminescence of control and test lines under UV light, owing to the presence of thioglycolic acid capped CdTe quantum dots. It has also been shown that the image processing based quantification of malaria will likely minimize the chances of false negative results under low parasite concentration and assist in early diagnosis of malaria.

PLPose: An efficient framework for detecting power lines via key points-based pose estimation

The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (∼29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/pl_mmpose).

Revolutionizing power line inspection: automated data acquisition through autonomous UAVs in simulated environment

Due to the growing need for electricity, the effective inspection of the power lines is becoming an important matter. In this paper, the author presents the inspection of power or transmission line with autonomous automatic UAVs (Unmanned Aerial Vehicles). For the comprehensive inspection of power lines and its different components, such as (cross arms, cracks in poles, rot damage, and insulator burn), It is needed to inspect from every side of the elements and the masts. So, the angle and speed of the drone are much more important to take images while moving around the poles. The simulator used for the experiments, including deep learning models, acts as a vital source of data analysis. At the same time, pictures are used as the primary data source. Through the Deep learning method, a suggestion of action generated for the movement around the masts. The use of a simulator is a quick, accurate, and inexpensive solution, with less real/world factors affecting the inspection process, such as weather, time, and cost of using many different resources. This study presents experiments with lightweight deep learning models through developing the prototype of vision based unmanned aerial vehicle to inspect the power line in a simulated environment. It focuses on the large demand of power companies to inspect the power line autonomously with the influence of deep learning. Finally, several deep learning models are compared when inspection along the power lines. The model shows satisfactory results in the testing path. The model trained by MobileNetV2 performs best among all other models.

An Early Warning Method of Transmission Line Icing Based on Power Grid Meteorological Monitoring Data

The icing of conductors may seriously affect the safe operation of the power grid, and it is imminent to research the early warning of icing of the power grid. This paper innovatively cites a mesoscale digital-intelligent weather forecast technology considering the influence of micro-topography and combining the power grid meteorological monitoring data to realize the monitoring and early warning of icing on the transmission line of the provincial power grid. The accuracy of the icing forecast can be improved through the interpolation correction of micro-topographic factors and the statistical correction of meteorological monitoring network monitoring. The study has achieved remarkable results in realizing precise line inspections and scientific ice-melting decision-making, enhancing the ability of power grid ice disaster prevention and mitigation, and has important theoretical significance and engineering application value for improving power grid security.

Obstacle and Collision Avoiding Drone

Abstract: Larger drones, such as those used for commercial operations, are much less likely to have obstacle avoidance features because they fly higher in the sky, where it is less likely that they will encounter static obstacles and more likely that they will encounter dynamic obstacles while also flying faster; the market’s offerings for static obstacle avoidance do not meet those needs. Since smaller drones are typically used for hobbyists and smaller-scale operations like thermal inspection, hazard detection inside of warehouses, and up-close inspection of wind turbines and power lines, obstacle avoidance is typically limited to these types of smaller drones. The performance requirements for Detect-and-Avoid capabilities for dynamic objects vary since infrastructure masking can occasionally reduce risks. For instance, you have an additional layer of protection when flying near structures or in a deep valley because manned aviation is less likely to be using the same airspace. Similar to this, infrastructure shadowing involves flying 50 feet or less away from a power line that is exactly 50 feet above the structure being “shadowed.” Given that you are more unlikely to come across any manned aircraft, this mitigation level is higher than just masking.

Design of an Over-Actuated Hexacopter Tilt-Rotor for Landing and Coupling in Power Transmission Lines

It is known that new power transmission line inspection techniques have been developed over the last few years with great potential to improve and, in some cases, even replace traditional inspection procedures such as using helicopters and cars. A series of Unmanned Aerial Vehicles (UAVs) such as fixed-wing or rotary-wing UAVs, and vehicles that climb on the power transmission line, promise to revolutionize the inspection market. In this light, at least 39 new research studies and/or products have been conducted and/or introduced to the market, respectively. However, in an incipient way, some works point to the fusion of some technologies: the development of multi-rotor UAVs and the ability to connect and move over the power transmission line. In line with this, the current work was proposed, with significant unprecedented advances (such as an over-actuated control capacity with tilt rotors, the capability of a displacement in the angle, and the maintenance of active motors on the power transmission line), and the design, modeling, and control of an over-actuated UAV able to move over the conductor cable without the need for a new locomotion system is presented. The aircraft allows for a greater response and the indispensable ability to approximate landing in a power transmission line arbitrary position rather than the catenary lowest point (due to its ability to forward/backward move using the tilting rotors). Its design is detailed, its subsystems are described, and its normal and coupled flight mode dynamics are modeled. The results show good stability and reliable maneuvers for the coupling-to-power-transmission-line flight mode, without any overshoots, and the ability to follow the entire catenary through different Real Control Action (RCA) sets.

Speed control strategy for power line inspection robot servo system considering time-varying parameters

Most servo systems in power line inspection robots consist of a motor, an independent joint, and a load. In the process of crossing obstacles, the parameters in the servo systems have conspicuous time-varying properties due to the posture changes. The time-varying properties of dynamic parameters and the flexibility of the load would cause the rotation speed of the inspection robot to fluctuate, thereby affecting the motion accuracy. In this paper, the pole placement strategy was proposed to optimize the parameters in the proportional integral (PI) controller. The optimal controller parameters were selected in different postures to ensure steady speed output in the inspection robot servo system. First, the dynamic equations of the inspection robot servo system were established. Both joint flexibility and load flexibility were considered in the modeling process. Then, the Arnoldi algorithm was used to reduce the order of the servo system, and the transfer function from the speed to the drive torque was obtained. Next, the controller parameters were optimized using the pole placement method. By reasonably selecting the pole damping coefficient, the inspection robot could obtain a stable speed output. Finally, the numerical analysis and speed control of the inspection robot in different postures were analyzed. The results showed that the control strategy of pole placement could achieve a stable rotation speed for the inspection robot.

A deep learning framework based on improved self‐supervised learning for ground‐penetrating radar tunnel lining inspection

AbstractIt is not practical to obtain a large number of labeled data to train a supervised learning network in tunnel lining nondestructive testing with ground‐penetrating radar (GPR). To decrease the dependence of supervised learning on the number of labeled data, an improved self‐supervised learning algorithm—self‐attention dense contrastive learning (SA‐DenseCL)—is proposed and incorporated with a mask region‐convolution neural network (Mask R‐CNN), which is trained by unlabeled and labeled GPR data. The proposed SA‐DenseCL adds a self‐attention‐based relevant projection head to the DenseCL architecture of self‐supervised learning, capturing the spatially continuing information between adjacent GPR traces. In the workflow, some unlabeled GPR images are used to pre‐train the SA‐DenseCL network for feature extraction and obtaining the backbone weights, which is superior to the conventional pre‐training methods of supervised learning pre‐trained by ImageNet images. The weights of the pre‐trained backbone are then used to initialize the Mask R‐CNN through transfer learning. Subsequently, a limited number of labeled GPR images are used to fine‐tune the Mask R‐CNN for automatically identifying the locations of the reinforcement bars and voids and estimating the secondary lining thickness. The experimental results show that the average precision reaches 96.70%, 81.04%, and 94.67% in identifying reinforcement bar locations, detecting void defects, and estimating secondary lining thickness, respectively, which outperform the conventional methods that use ImageNet‐based supervised learning or GPR image‐based DenseCL for initializing the Mask R‐CNN backbone weights. It is observed that the improved self‐supervised learning‐based framework can improve the detection and estimation accuracy in GPR tunnel lining inspection.

Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology

Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes it difficult to meet the engineering demand for fast inspection of transmission lines. In view of this, this paper proposes an automatic spacer bar segmentation algorithm, CM-Mask-RCNN, that combines the CAB attention mechanism and MHSA self-attention mechanism, which automatically extracts the spacer bars and calculates the center coordinates, and combines classical algorithms such as beam method leveling, spatial front rendezvous, and spatial curve fitting, based on UAV inspection video data, to realize arc sag measurement with a low cost and high efficiency. It is experimentally verified that the CM-Mask-RCNN algorithm proposed in this paper achieves an AP index of 73.40% on the self-built dataset, which is better than the Yolact++, U-net, and Mask-RCNN algorithms. In addition, it is also verified that the adopted approach of fusing CAB and MHSA attention mechanisms can effectively improve the segmentation performance of the model, and this combination improves the model performance more significantly compared with other attention mechanisms, with an AP improvement of 2.24%. The algorithm in this paper was used to perform arc sag measurement experiments on 10 different transmission lines, and the measurement errors are all within ±2.5%, with an average error of −0.11, which verifies the effectiveness of the arc sag measurement method proposed in this paper for transmission lines.

Overhead transmission line condition assessment based on intention classification and slot filling using optimized BERT model

As the key equipment in power system, the running state of overhead transmission lines is affected by various complex and random factors, and the maintenance workload of the line is too heavy to overhaul regularly. Therefore, it is very necessary to build a refined condition assessment system to improve the diagnosis accuracy and maintenance efficiency of overhead transmission lines. Recent years, State Grid Corporation of China (SGCC) has recorded mass of monitoring reports of transmission lines. The natural language processing (NLP) with deep learning model provides an effective way to extract the key defect information from the monitoring reports. In this paper, the joint model of intention classification and slot filling (ICSF) based on bidirectional encoder representation from transformers (BERT) is introduced. To improve the precision of defect information extraction, two optimization models of BERT are presented. The results show that Robustly Optimized BERT Pre-Training Approach (RoBERTa) has achieved better effects on ICSF with the extraction accuracy of 92.22%. Then, the hierarchical weighted scoring method is introduced to score the status of overhead transmission line based on the results of ICSF-RoBERTa. And the assessment results of the overhead transmission line state and the corresponding maintenance strategies are provided according to the scores. Finally, the feasibility of the proposed method is validated by practical cases of line inspection reports.

A power line segmentation model in aerial images based on an efficient multibranch concatenation network

Inspection of a power transmission line by an unmanned aerial vehicle (UAV) is an important measure to ensure the operational safety of the power grid. A semantic segmentation model based on deep learning can assist UAVs in extracting power lines, which is a crucial task to ensure the safe navigation of UAVs. However, the existing models are vulnerable to complex background interference in aerial images and the thin structure of power lines. To solve these problems, an improved power line segmentation model based on Deeplabv3+ (PL-Deeplab) is proposed in this paper. To create our model, the multibranch concatenation network (MCNet) with stronger feature extraction capability is introduced to the backbone network. Then, we design a one-shot aggregation feature pyramid (OSAFP) to effectively extract the contextual information and provide a larger receptive field. Furthermore, the feature fusion module (FFM) in the decoder is used to increase the utilization of high-low level feature information and improve the ability of the model to accurately segment the boundaries of power lines. In addition, the weighted loss function is used to solve the problem of class imbalance in the power lines dataset. A large number of experiments on the power lines public dataset show that the proposed model can complete the power line inspection task better and provide security for the UAV power line inspection.

Development of a series of miniaturization device for insulation wrapping of distribution lines

With the continuous expansion of the scale of power grid in China, the traditional line inspection method has been difficult to meet the requirements of the future power system, the research and application of line inspection robot has become inevitable.Line inspection robot instead of manual line inspection, not only can avoid unnecessary dangers, but also can complete insulation repair work, inspection inspection efficiency is high [1-2]. This project by 10 kv overhead insulated package unit as the research object, according to the insulation repair the actual situation of the construction site, developed a device used for 10 kv overhead line insulation package, the robot by the ground staff through tablet computer remote control operation, to walk to the on-line control insulation package device to fix position, to repair the cable package on the wire. The whole operation process does not need power outage, so that the robot can replace the manual work to complete the insulation repair of the line [3-4].

UAV High-Voltage Power Transmission Line Autonomous Correction Inspection System Based on Object Detection

With the development of technology, unmanned aerial vehicles (UAVs) are playing an increasingly important role in the inspection of high-voltage power transmission line. The traditional inspection method relies on the operator to manually control the drone for inspection. Although many companies are using real-time dynamic carrier phase differencing technology to achieve high-precision positioning of UAVs, when UAVs fly autonomously at high altitudes to photograph specific objects, the objects tend to deviate from the center of the picture. To address this error, in this paper, an autonomous UAV inspection system based on object detection is designed. (1) To detect inspection objects, the corresponding dataset is established on the basis of the UAV autonomous inspection task. (2) To obtain the position information of the target object, a lightweight object detector based on the YOLOX network model is designed. First, the backbone is replaced with MobileNetv3. Next, in the neck structure, the Ghost module is introduced and depthwise convolution is applied instead of normal convolution. Then, to embed the location information into the channel attention, coordinate attention is introduced after the output feature layer of the backbone, enabling the lightweight network to operate on a larger area of focus. Finally, to improve the accuracy of the bounding box regression, the α-DIOU loss function is introduced. (3) To obtain the best image acquisition position, the results of object detection combined with the real-time status of the UAV are used. (4) To enable the UAV to complete the final corrections, position control and altitude control are used. Compared to the original YOLOX tiny, the new model improves the mAP_0.5:0.95 metric by about 2 percentage points, with a significant reduction in the number of parameters and computation, while running at 56 FPS on Nvidia NX. This system can effectively solve the problem of the target deviating from the center of the picture when the UAV takes pictures during a high-altitude autonomous inspection, verified by many actual flight experiments.