Search Results (205)

Due to increasing human activities underwater, there is a growing demand for high-speed underwater optical communication (UOWC) data links for security surveillance, environmental monitoring, pipeline inspection, and other applications. Line-of-sight communication is impossible under certain conditions due to misalignment, physical obstructions, irregular usage, and difficulty adjusting the receiver orientation, especially when used in environments with mobile users or submerged sensor networks. Therefore, non-line-of-sight (NLOS) optical communication is used in this study. Advanced modulation schemes—quadrature amplitude modulation and orthogonal frequency-division multiplexing (QAM-OFDM)—were used to transmit the signal underwater between two network nodes. QAM increases the data transfer rate, while OFDM reduces dispersion and inter-symbol interference (ISI). The proposed UOWC system is investigated using a 532 nm green laser diode (LD). Reliable high-speed data transmission of up to 15 Gbps is achieved over horizontal distances of 134 m, 43 m, 21 m, and 5 m in four different aquatic environments—pure water (PW), clear ocean (CLO), coastal ocean (COO), and harbor II (HarII), respectively. The system achieves effectively error-free performance within the simulation duration (BER < 10⁻⁹), with a received optical signal power of approximately −41.5 dBm. Clear constellation patterns and low BER values are observed, confirming the robustness of the proposed architecture. Despite the limitations imposed by non-line-of-sight (NLOS) communication and the diversity aquatic environments, our proposed architecture excels at underwater long-distance data transmission at high speeds. Full article

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a ${\mathrm{mAP}}_{50}$ of 78.3% and ${\mathrm{mAP}}_{50–95}$ of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections. Full article

Semantic segmentation of transmission-line point clouds is fundamental to intelligent power inspection and grid asset management, as segmentation accuracy directly influences defect detection and facility assessment tasks. However, transmission-line point clouds collected across different voltage levels often show significant variations in density and geometric structure due to heterogeneous LiDAR sensors and flight configurations. Combined with the high cost of large-scale manual annotation, these factors limit the scalability of existing supervised segmentation methods. To overcome these challenges, we propose a geometry-consistency-guided unsupervised domain adaptation framework tailored for cross-voltage transmission-line point-cloud segmentation. The framework employs KPConvX as the backbone and integrates three progressive components. First, a geometric consistency constraint enhances robustness to spatial variations and enables extraction of structural features invariant across voltage levels. Second, a domain feature alignment module reduces distribution shifts through global feature transformation. Third, a minimum-entropy-based pseudo-label refinement strategy improves the reliability of pseudo-labels during self-training. Experiments on a multi-voltage transmission-line dataset demonstrate the effectiveness of the proposed method. With the KPConvX backbone, the framework achieves 66.1% mean Intersection over Union (mIoU) and 94.3% overall accuracy on the unlabeled 110 kV target domain, exceeding the source-only baseline by 15.6% mIoU and outperforming several state-of-the-art UDA methods. This work provides an efficient, annotation-friendly solution for cross-voltage point-cloud segmentation and offers a promising direction for domain adaptation in complex power-grid environments. Full article

To maintain reliability, safety, and sustainability in power transmission, insulator defect detection has become a critical task in power line inspection. Due to the complex backgrounds and small defect sizes encountered in insulator defect images, issues such as false detections and missed detections often occur. The existing You Only Look Once (YOLO) object detection algorithm is currently the mainstream method for image-based insulator defect detection in power lines. However, existing models suffer from low detection accuracy. To address this issue, this paper presents an improved YOLOv5-based MC-YOLO insulator detection algorithm. To effectively extract multi-scale information and enhance the model’s ability to represent feature information, a multi-scale attention convolutional fusion (MACF) module incorporating an attention mechanism is proposed. This module utilises parallel convolutions with different kernel sizes to effectively extract features at various scales and highlights the feature representation of key targets through the attention mechanism, thereby improving the detection accuracy. Additionally, a cross-context feature fusion module (CCFM) is designed, where shallow features gain partial deep semantic supplementation and deep features absorb shallow spatial information, achieving bidirectional information flow. Furthermore, the Spatial-Channel Dual Attention Module (SCDAM) is introduced into CCFM. By incorporating a dynamic attention-guided bidirectional cross-fusion mechanism, it effectively resolves the feature deviation between shallow details and deep semantics during multi-scale feature fusion. The experimental results show that the MC-YOLO algorithm achieves an mAP@0.5 of 67.4% on the dataset used in this study, which is a 4.1% improvement over the original YOLOv5. Although the FPS is slightly reduced compared to the original model, it remains practical and capable of rapidly and accurately detecting insulator defects. Full article

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications. Full article

The inspection of power transmission lines using unmanned aerial vehicles primarily relies on object detection. However, the continuous emergence of new obstacle types necessitates frequent updates to detection models, leading to substantial retraining costs. To address this challenge, we propose a novel framework named IViT, which integrates incremental learning with a hybrid CNN-Transformer architecture for improved identification. We combined knowledge distillation with the elastic response selection distillation strategy to enhance detection performance for old classes and strengthen knowledge retention through star convolutional residual blocks constructed via element-wise multiplication. We designed a separable convolution aggregation block that integrates PConv with an attention mechanism, effectively merging global and local information to improve detection accuracy. Finally, we unified the two modules into a hybrid block. In the static detection task, IViT achieves a mAP of 55.3%, a mAP₅₀ of 83.6%, and a mAP₇₅ of 61.0%. For the incremental detection task, it attains a mAP of 57.8%, a mAP₅₀ of 79.7%, and a mAP₇₅ of 62.3%. Extensive experiments on the transmission line corridor external damage dataset and the INSPLAD dataset demonstrate that IViT exhibits outstanding detection performance compared to mainstream static object detection models and incremental object detection models. Full article

Dynamic social and legal transformations drive technological innovation and the transition of energy and heating sectors toward renewable sources and higher efficiency. Ensuring the reliable operation of these systems requires regular inspections, fault detection, and infrastructure maintenance. Unmanned Aerial Vehicles (UAVs) are increasingly being used for monitoring and diagnostics of photovoltaic and wind farms, power transmission lines, and urban heating networks. Based on literature from 2015 to 2025 (Scopus database), this review compares UAV platforms, sensors, and inspection methods, including thermal, RGB/multispectral, LiDAR, and acoustic, highlighting current challenges. The analysis of legal regulations and resulting operational limitations for UAVs, based on the frameworks of the EU, the US, and China, is also presented. UAVs offer high-resolution data, rapid coverage, and cost reduction compared to conventional approaches. However, they face limitations related to flight endurance, weather sensitivity, regulatory restrictions, and data processing. Key trends include multi-sensor integration, coordinated multi-UAV missions, on-board edge-AI analytics, digital twin integration, and predictive maintenance. The study highlights the need to develop standardised data models, interoperable sensor systems, and legal frameworks that enable autonomous operations to advance UAV implementation in energy and heating infrastructure management. Full article

Aiming at the problems of complex background interference and partial occlusion of fault targets during UAV transmission line inspection, this paper proposes an MRA-YOLOv8-based fault detection method for transmission line components. Firstly, the YOLOv8 network is adopted as the baseline framework, and a self-attention mechanism is incorporated into its backbone network to enhance the detection accuracy for occluded targets. Subsequently, a Multi-scale Attention Aggregation module is introduced into the neck network to improve the feature extraction capability for fault targets against complex backgrounds. Furthermore, the bounding box loss function is optimized to mitigate class imbalance issues, thereby boosting the model’s fault detection performance. Finally, the proposed algorithm is validated using inspection data collected over the past three years from an electric power inspection department. Experimental results demonstrate that the proposed method achieves an average detection precision of 92.5% and a recall rate of 90.9%. Full article

Composite interphase spacers are essential components in ultra-high-voltage (UHV) transmission lines to suppress conductor galloping. This study investigates the first reported case of a core-rod fracture in a 500 kV composite spacer and elucidates its degradation mechanism through multi-scale characterization, electrical testing combined and electric field and mechanical simulation. Macroscopic inspection and industrial computed tomography (CT) show that degradation initiated at the unsheltered high-voltage sheath–core interface and propagated axially, accompanied by continuous interfacial cracks and void networks whose volume ratio gradually decreased along the spacer. Material characterizations indicate moisture-driven glass-fiber hydrolysis, epoxy oxidation, and progressive interfacial debonding. Leakage current test further indicates humidity-sensitive conductive paths in the degraded region, confirming the presence of moisture-activated interfacial channels. Electric-field simulations under two shed configurations demonstrated that local field intensification was concentrated within 20–30 cm of the HV terminal, where the sheath and core surface fields increased by approximately 9.3% and 5.5%. Mechanical modeling demonstrates a pronounced bending-induced stress concentration at the same end region. The combined effects of moisture ingress, electrical stress, mechanical loading, and chemical degradation lead to the decay-like fracture. Improving sheath hydrophobicity, enhancing interfacial bonding, and optimizing end-fitting geometry are recommended to mitigate such failures and ensure the long-term reliability of UHV composite interphase spacers. Full article

To address the edge blurring issue of drone inspection images of mountainous transmission lines caused by non-uniform haze interference, as well as the low operational efficiency of traditional dehazing algorithms due to increased network complexity, this paper proposes a high-frequency guided dual-branch attention multi-scale hierarchical dehazing network for transmission line scenarios. The network adopts a core architecture of multi-block hierarchical processing combined with a multi-scale integration scheme, with each layer based on an asymmetric encoder–decoder with residual channels as the basic framework. A Mix structure module is embedded in the encoder to construct a dual-branch attention mechanism: the low-frequency global perception branch cascades channel attention and pixel attention to model global features; the high-frequency local enhancement branch adopts a multi-directional edge feature extraction method to capture edge information, which is well-adapted to the structural characteristics of transmission line conductors and towers. Additionally, a fog density estimation branch based on the dark channel mean is added to dynamically adjust the weights of the dual branches according to haze concentration, solving the problem of attention failure caused by attenuation of high-frequency signals in dense haze regions. At the decoder end, depthwise separable convolution is used to construct lightweight residual modules, which reduce running time while maintaining feature expression capability. At the output stage, an inter-block feature fusion module is introduced to eliminate cross-block artifacts caused by multi-block processing through multi-strategy collaborative optimization. Experimental results on the public datasets NH-HAZE20, NH-HAZE21, O-HAZE, and the self-built foggy transmission line dataset show that, compared with classic and cutting-edge algorithms, the proposed algorithm significantly outperforms others in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM); its running time is 19% shorter than that of DMPHN. Subjectively, the restored images have continuous and complete edges and high color fidelity, which can meet the practical needs of subsequent fault detection in transmission line inspection. Full article

To address the problem of low detection accuracy caused by uneven brightness distribution in transmission line inspection images under complex lighting conditions, this paper proposes an unsupervised image enhancement method that integrates grayscale feature guidance and luminance consistency loss constraint. First, a U-shaped generator combining a bottleneck structure with large receptive field depthwise separable convolutions is designed to efficiently extract multi-scale features. Second, a grayscale feature-guided image generation module is incorporated into the generator, using grayscale information to adaptively enhance local low-light regions and effectively suppress overexposed regions. Meanwhile, to accommodate the characteristics of unpaired data training, a luminance consistency loss is introduced. By constraining the global luminance distribution consistency between the generated image and the reference image, the overall brightness balance of the generated image is improved. Finally, a multi-level discriminator structure is constructed to enhance the model’s ability to distinguish global and local luminance in the generated images. Experimental results show that the proposed method significantly improves image quality (PSNR increased from 7.73 to 18.41, SSIM increased from 0.43 to 0.85). Furthermore, the enhanced images lead to improvements in defect detection accuracy. Full article

Unmanned Aerial Vehicle (UAV)-based inspection of transmission line insulators faces significant challenges due to complex backgrounds, variable imaging conditions, and diverse defect characteristics. Existing deep learning approaches often fail to balance detection accuracy with computational efficiency for edge deployment. This paper presents MCP-YOLO (Multi-scale Complex-background Pruned YOLO), a lightweight yet accurate detection framework specifically designed for real-time insulator defect identification. The proposed framework introduces three key innovations: (1) MS-EdgeNet module that enhances multi-granularity edge features through grouped convolution, improving detection robustness in cluttered environments; (2) Dynamic Feature Pyramid Network (DyFPN) that combines dynamic upsampling with re-parameterized multi-branch architecture, enabling effective multi-scale defect detection; (3) Auxiliary detection head that provides additional supervision during training while maintaining inference efficiency. Furthermore, Group SLIM pruning is employed to achieve model compression without sacrificing accuracy. Extensive experiments on a real-world dataset of 3091 UAV-captured images demonstrate that MCP-YOLO achieves 92.1% mAP@0.5, 90.5% precision, and 89.0% recall, while maintaining only 8.65 M parameters. Compared to state-of-the-art detectors, the proposed method achieves superior detection performance with significantly reduced computational overhead, reaching 250 FPS inference speed. The model size reduction of 37.3% from the baseline, coupled with enhanced detection capabilities, validates MCP-YOLO’s suitability for practical deployment in automated power grid inspection systems. Full article

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 data of power transmission lines. However, existing methods face two core challenges: at the data level, insulator defect samples are extremely scarce in massive image datasets, leading to severe data imbalance issues. At the algorithm level, deep learning-based defect detection methods rely on data-driven feature extraction, ignoring quantifiable prior knowledge such as insulator installation specifications and mechanical structure. This factor results in low localization efficiency and poor robustness in complex scenarios. To address these issues, this paper proposes an insulator defect detection method based on Vision–Language models and prior knowledge. It extracts prior knowledge about the physical characteristics of insulators, quantifies spatial structure and installation specifications as prior constraints, embeds prior knowledge into the vision–language model’s feature space to generate insulator defect samples, addresses the data imbalance issue, and detects insulator defects using an improved You Only Look Once (YOLO) algorithm. This approach reduces model parameters while maintaining detection accuracy, constructing a lightweight model for insulator defect detection. The experimental results show that, compared with PP-YOLOE-m and RT-DETR-R18 models, the method proposed in this paper can significantly improve the detection accuracy. The mean average precision indicator of the model in this paper has reached 95.7%. Full article

Detecting corroded metal fittings in UAV-based transmission line inspections is challenging due to the small object size and environmental interference, causing high false and missed detection rates. To address these, this paper proposes a novel knowledge-distilled lightweight YOLO model, integrating a densely-connected convolutional network and spatial pixel-aware self-attention mechanism in the teacher model training stage to enhance feature transfer and structured feature utilization for reducing environmental interference, while employing the lightweight MobileNet as the feature extractor in the student model training stage and optimizing candidate box migration via the teacher model’s efficient intersection-over-union non-maximum suppression (EIoU-NMS). This model overcomes the challenges of small-object fitting detection in complex environments, improving fault identification accuracy and reducing manual inspection costs and missed detection risks, while its lightweight design enables rapid deployment and real-time detection on UAV terminals, providing a reliable technical solution for unmanned smart grid operation. Experimental results on actual UAV inspection images demonstrate that the model significantly enhances detection accuracy, reduces false and missed detections, and achieves faster speeds with substantially fewer parameters, highlighting its outstanding effectiveness and practicality in power system maintenance scenarios. Full article

To improve the efficiency of multi-region multi-unmanned aerial vehicle (UAV) inspection, this paper proposes a composite task planning strategy integrating the K-Means++ genetic algorithm (KMGA) and the multi-neighborhood iterative dynamic programming (MNIDP) method. Firstly, the multi-region multi-UAV inspection problem is modeled as a multiple traveling salesmen problem with neighborhoods (MTSPN). Then, this problem is decomposed into two interrelated subproblems to mitigate the complexity inherent in the solution process: that is, the multiple traveling salesmen problem (MTSP) and multi-neighborhoods path planning (MNPP) problem. Based on this decomposition, the MTSP is solved by the KMGA by converting it into m spatially non-overlapping traveling salesmen problems (TSPs) and then these TSPs are solved to obtain the approximate optimal visiting sequences for the nodes in each TSP in a short time. Subsequently, the MNPP can be efficiently solved by an MNIDP which plans the paths between the corresponding neighborhood of each node based on the node visiting sequences, thus obtaining the approximate optimal path length of the MTSPN. The simulation results demonstrate that the proposed composite strategy exhibits advantages in computational efficiency and optimal path length. Specifically, compared to the baseline algorithm, the average tour length obtained by the KMGA decreased by 23.24%. Meanwhile, the average path lengths computed by MNIDP in three instances were reduced from 8.00% to 11.41% and from 6.46% to 10.08% compared to two baseline algorithms, respectively. It provides an efficient task and path planning solution for multi-region multi-UAV operations in power transmission line inspections, thereby enhancing inspection efficiency. Full article