Line Inspection Research Articles

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance.

Human-machine collaborative inspection of transmission corridors based on the BeiDou positioning

Abstract Inadequate inspection performance of transmission lines leads to damages, posing threats to the safety of power grids. To enhance the efficiency of transmission line inspection and to reduce safety hazards, a method based on the BeiDou positioning for human-machine collaboration in transmission line inspection is proposed. Drones equipped with then BeiDou positioning are utilized to collect transmission line data via the Web Mercator projection, determining the inspection routes. Fault parameters of transmission lines are extracted for manual monitoring, identifying overlay current and voltage frequency fault characteristics. The optimal inspection route is then determined based on these features, achieving human-machine collaboration in transmission line inspection. Experimental results demonstrate that the proposed method detected 11 fault nodes, with inspection paths closely matching the transmission line structure. It identified six defects in power system transmission lines, with an inspection time of only 2.3 seconds. The area under the ROC curve, encompassing true positive and false positive rates, is approximately 5000, indicating the method’s effectiveness, efficiency, and accuracy in inspection.

Spatiotemporal Precise Routing Strategy for Multi-UAV-Based Power Line Inspection With Integrated Satellite-Terrestrial Network

Spatiotemporal Precise Routing Strategy for Multi-UAV-Based Power Line Inspection With Integrated Satellite-Terrestrial Network

Further Application Of Automatic Control Based On Servo Motor In Smart Grid

The paper summarize the advantages and applications of servo motor. As a kind of equipment that can replace manual long-distance inspection, the intelligent inspection device for overhead transmission lines can significantly improve inspection efficiency and reduce labor intensity. The servo motor with high power density and low torque ripple can guarantee the high precision and stable operation of the intelligent inspection device in line inspection, which provides a certain practical intelligent application significance for transmission line inspection.

Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning

The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection.

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.

Detection Method for Bolt Missing Pins of Transmission Line Based on Improved SSD Network

The bolt pin structure plays a role in connecting and fixing various components in transmission lines, and the pins are prone to detachment, which may lead to the disintegration of key components and cause serious consequences. The bolt pin target size in the transmission line inspection image is small, the background is complex, and the detection effect using traditional object detection algorithms is poor. This article proposes a method for detecting missing bolts in transmission lines based on an improved SSD (Single Shot MultiBox Detector) network. Using the traditional SSD network as the foundation, a feature fusion module is first added to the network to enrich the semantic information of low-level feature maps and the detail information of high-level feature maps through the mutual fusion of high-level and low-level feature maps. Secondly, embedding a channel attention mechanism module enhances the channel features corresponding to the bolt pin structure and suppresses channel features that are irrelevant to the background; At the same time, the introduction of Focal Loss loss function reduces the unevenness of positive and negative sample classification. Finally, experiments were conducted on the collected bolt missing pin detection dataset, and the results showed that the method proposed in this paper has good performance in detecting missing pins, and the detection accuracy has been significantly improved.

Prototype for Multi-UAV Monitoring–Control System Using WebRTC

Most unmanned aerial vehicle (UAV) ground control station (GCS) solutions today are either web-based or native applications, primarily designed to support a single UAV. In this paper, our research aims to provide an open, universal framework intended for rapid prototyping, addressing these objectives by developing a Web Real-Time Communication (WebRTC)-based multi-UAV monitoring and control system for applications such as automated power line inspection (APOLI). The APOLI project focuses on identifying damage and faults in power line insulators through real-time image processing, video streaming, and flight data monitoring. The implementation is divided into three main parts. First, we configure UAVs for hardware-accelerated streaming using the GStreamer framework on the NVIDIA Jetson Nano companion board. Second, we develop the server-side application to receive hardware-encoded video feeds from the UAVs by utilizing a WebRTC media server. Lastly, we develop a web application that facilitates communication between clients and the server, allowing users with different authorization levels to access video feeds and control the UAVs. The system supports three user types: pilot/admin, inspector, and customer. Our research aims to leverage the WebRTC media server framework to develop a web-based GCS solution capable of managing multiple UAVs with low latency. The proposed solution enables real-time video streaming and flight data collection from multiple UAVs to a server, which is displayed in a web application interface hosted on the GCS. This approach ensures efficient inspection for applications like APOLI while prioritizing UAV safety during critical scenarios. Another advantage of the solution is its integration compatibility with platforms such as cloud services and native applications, as well as the modularity of the plugin-based architecture offered by the Janus WebRTC server for future development.

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

A-346 Evaluating 14-3-3η Detection in Finger Prick Blood Using Lateral Flow Technology: Advancing Clinical Laboratory Diagnostics

Abstract Background The protein 14-3-3η is a critical biomarker in the diagnosis and monitoring of autoimmune rheumatologic disorders, and its early detection is pivotal for patient outcomes. Previous research has established the significance of 14-3-3η as a biomarker, particularly in inflammatory polyarthritis, as well as patients’ and physicians’ interest in near-patient testing. While 14-3-3η is traditionally measured in serum via ELISA, this study explores the innovative use of lateral flow technology (LFT) for its detection in finger prick blood, addressing the urgent need for near-patient testing.This research aims to evaluate the technical and clinical performance of 14-3-3η measurement using LFT in comparison to standard ELISA methods, with an emphasis on its detectability in finger prick blood. Methods This study included two phases of testing involving patient serum and spiked whole blood. In the serum phase, 53 samples (19 NEG, ≤0.19 ng/mL, and 34 POS, 0.27 to 28 ng/mL 14-3-3η) were tested using LFT. LFT results were compared to JOINTstat® ELISA measurements for concordance. For whole blood, recombinant 14-3-3η and ELISA-positive serum samples were spiked into whole blood at increasing concentrations (0.4 to 3.2 ng/mL) and tested with LFT. Concordance criteria included visual inspection of test and control lines, with results captured and analyzed using a reference key card. The study was extended to assess 14-3-3η levels in consented patients with 22 matched finger prick and venous serum samples​​. Results In serum testing, LFT demonstrated 100% positivity in detecting 14-3-3η for POS samples. Among NEG samples, 85% tested negative, with 15% positive, indicating a strong concordance between LFT and ELISA. The LFT's ability to semi-quantitatively measure 14-3-3η was confirmed by a significant Kruskal-Wallis test (p<0.0001) across four intensity groups. In whole blood testing, LFT correctly identified all samples spiked at concentrations ≥0.5 ng/mL. The Spearman correlation showed a strong and significant association (r=0.96 to 0.98, p=0.001) between LFT test line intensity and spiked 14-3-3η levels. Finally, the equivalence testing for finger prick and venous serum samples affirmed the LFT’s reliability, with a strong correlation (p<0.0001), underscoring its potential for near-patient testing​​. Conclusions The study validates LFT as a reliable and semi-quantitative method for detecting 14-3-3η in finger prick blood, demonstrating strong concordance with traditional ELISA measurements. The findings support the feasibility of LFT in near-patient testing for early detection, prognosis, and monitoring of autoimmune diseases, paving the way for enhanced patient accessibility. Future research will focus on integrating this technology into routine clinical care and examining its impact on patient management and outcomes.

The Method but Not the Protocol Affects Lactate-Threshold Determination in Competitive Swimmers.

The study validated variables corresponding to lactate threshold (LT) in swimming. Speed (sLT), blood lactate concentration (BLLT), oxygen uptake (VO2LT), and heart rate (HRLT) corresponding to LT were calculated by 2 different incremental protocols and validated in comparison with maximal lactate steady state (MLSS). Ten competitive swimmers performed a 7 × 200-m front-crawl incremental "step test" with 2 protocols: (1)with 30-second rests between repetitions (short-rest incremental protocols) and (2)on a 5-minute cycle (swim + rest time, long-rest incremental protocols). Five methods were used for the assessment of sLT and corresponding BLLT, VO2LT, and HRLT: intersection of 2 lines, Dmax, modified Dmax, visual inspection, and intersection of combined linear and exponential regression lines. Subsequently, swimmers performed two to three 30-minute continuous efforts to identify speed (sMLSS) and physiological parameters corresponding to MLSS. Both protocols resulted in similar sLT and corresponding physiological variables (P > .05). Bland-Altman plots showed agreement between protocols (sLT, bias: -0.017 [0.002]m·s-1; BLLT, bias: 0.0 [0.5]mmol·L-1; VO2LT, bias: -0.1 [2.2]mL·kg-1·min-1; HRLT. bias: -2 [8]beats·min-1). However, sLT calculated by modifiedDmax using short rest was higher compared with speed at MLSS (1.346 [0.076] vs 1.300 [0.101]m·s-1; P < .05). Calculated sLT, BLLT, VO2LT, and HRLT using all other methods in short-restand long-rest incremental protocols were no different compared with MLSS (P > .05). Both 7 × 200-m protocols are valid for determination of sLT and corresponding physiological parameters, but the modifiedDmax method may overestimate sLT.

Application of End-to-End Perception Framework Based on Boosted DETR in UAV Inspection of Overhead Transmission Lines

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

Dynamic modelling and a model-based control strategy of joint servo system for power transmission line inspection robot

Abstract The time-varying load inertia is one of the important dynamic characteristics of the power transmission line inspection robots (PTLIRs) in joint space, which can heavily affect the stability of the robot in the inspection process. In this paper, with considering the time-varying characteristics of load inertia, a method for modelling and control of the flexible joint servo system of the PTLIR is studied. First, the control system of the PTLIR is established based on the two inertia system. In order to calculate the load inertia, a dynamic model of the inspection robot is established. Then, the model-based notch filter is designed to improve dynamic performance of the robot, and suppress the vibration amplitude change which caused by the time-varying load inertia. Finally, the comparison of the control performance between the traditional control strategy and the model-based control strategy with notch filter are compared. The simulation results show that the paper can provide an effective tool for modelling and improving the stability of the PTLIR.

SONet: A Small Object Detection Network for Power Line Inspection Based on YOLOv8

SONet: A Small Object Detection Network for Power Line Inspection Based on YOLOv8

QYOLO: Contextual Query-Assisted Object Detection in High-Resolution Images

High-resolution imagery captured by drones can detect critical components on high-voltage transmission towers, providing inspection personnel with essential maintenance insights and improving the efficiency of power line inspections. The high-resolution imagery is particularly effective in enhancing the detection of fine details such as screws. The QYOLO algorithm, an enhancement of YOLOv8, incorporates context queries into the feature pyramid, effectively capturing long-range dependencies and improving the network’s ability to detect objects. To address the increased network depth and computational load introduced by query extraction, Ghost Separable Convolution (GSConv) is employed, reducing the computational expense by half and further improving the detection performance for small objects such as screws. The experimental validation using the Transmission Line Accessories Dataset (TLAD) developed for this project demonstrates that the proposed improvements increase the average precision (AP) for small objects by 5.5% and the F1-score by 3.5%. The method also enhances detection performance for overall targets, confirming its efficacy in practical applications.

Fault detection method for transmission line components based on lightweight GMPPD-YOLO

Abstract This paper designs a lightweight high-precision transmission line component detection model, named grouped dense, monotonic self-regularized, and partial faster convolution, pruning, and distillation optimized—you only look once (GMPPD-YOLO), in transmission line inspection. It addresses the issue of low detection accuracy of target detection algorithms due to the complex background, large differences in target shape, location, texture, etc, as well as diversified and smaller defects in insulator and vibration hammer images taken by unmanned aerial vehicles from multiple angles. To enhance the model’s feature extraction capabilities in complex backgrounds and across different scales, the grouped dense C3 dense feature extraction module was designed, enabling the model to more effectively handle diverse defect forms. Simultaneously, the monotonic self-regularized pyramid pooling–fast (MSPPF) module is proposed to enhance the model’s capability to process multi-scale information. Additionally, the partial-faster C3 feature awareness module is designed to improve feature fusion performance, enhancing the model’s ability to perceive features at different scales. Finally, channel pruning was used to reduce redundant parameters, and knowledge distillation was employed to compensate for the accuracy loss caused by pruning. This approach further compressed the model size while ensuring its detection performance. The experimental results demonstrate that compared to the original YOLOv5s algorithm, the proposed GMPPD-YOLO algorithm achieves a reduction in parameters by 68.4%, a decrease in Giga floating-point operations per second by 58.2%, and a reduction in the model size by 66.4%, while achieving an increase in precision by 1%, mAP50 by 1.1%, and mAP95 by 0.4%. This confirms the significant potential of the GMPPD-YOLO algorithm for deployment in real-time drone-based power transmission line inspections.

Improved Lightweight YOLOv5 Algorithm for Detecting Grid Inspection Object

Abstract In the field of unmanned aerial vehicle-based transmission line inspection, the neural network models currently employed suffer from large model sizes and high computational requirements, resulting in a trade-off between detection speed and accuracy. We propose an improved lightweight YOLOv5 object detection algorithm based on channel pruning to address this issue. The approach incorporates the SimAM attention mechanism into the backbone network to enhance feature extraction capability without increasing computational complexity, thereby improving detection accuracy. Traditional convolutional modules in the neck network are replaced with GSconv modules to reduce convolutional computation and enhance detection speed. The Wise-IoU loss is utilized as the localization loss for the detection model, speeding up model convergence. Finally, a model compression technique is implemented to eliminate unnecessary channels within the network, resulting in a decrease in model size and a boost in detection speed. Experimental results on a constructed grid inspection dataset demonstrate that the improved model achieves a 72.1% reduction in model size, a 62.5% reduction in GFLOPs, a 5.9 FPS improvement, and a 0.3% mAP increase compared to the original YOLOv5. These findings underscore the advantages of the improved lightweight YOLOv5 object detection algorithm, offering a blend of heightened accuracy, streamlined size, and swift detection capabilities.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Lightweight transmission line defect identification method based on OFN network and distillation method

AbstractEdge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application.