Search Results (284)

Over the past decade, drone-based aerial inspection of overhead power lines has proven superior to traditional ground-based methods. However, in flatland areas, it remains costlier, as total expenses include not only flights but also extensive mission planning. Operators must select takeoff zones and conduct flights in compliance with weather conditions and numerous regulations. Automating mission planning can reduce operator workload, lower the risk of rule violations, and boost inspection efficiency. This paper introduces a framework for automating power line inspection route planning. It selects takeoff areas and generates drone routes for specified line segments, which meet all regulatory requirements. The framework incorporates a novel method for automatic pole-type identification using satellite imagery. The approach combines a YOLO detector, trained on synthetic data, with an expert system, resulting in a 36.9% improvement in performance (on the tested dataset) compared to prior solutions. The final solution was implemented as an open-source QGIS plugin. The experimental results demonstrate that the automated path-planning approach successfully generates inspection routes for line segments exceeding 50 km (135 poles) and increases the number of inspected poles by 58.7%, enabling the capture of power line insulators, which can then be automatically segmented and analyzed using machine learning algorithms. Full article

Routine inspections of transmission lines are essential for maintaining the reliability of the power grid. Airborne LiDAR technology provides detailed 3D corridor data for automated hazard detection, such as vegetation encroachment and structural anomalies. However, manually analyzing large point clouds is inefficient, and current segmentation methods struggle with scene complexity, scale variation, and the high cost of annotation. In this study, we present a label-efficient segmentation method built on MinkUNet, a sparse voxel convolutional network enhanced with self-attention modules in its encoder–decoder for better spatial reasoning over corridor objects (e.g., trees, buildings, towers). To further handle structural diversity and class imbalance, we adopt task-specific data augmentations and focal loss. A multi-stage pseudo-labeling strategy is then employed to enable effective cross-scene generalization with minimal labeled data. We validate our method on three real-world transmission line datasets. On the Foshan dataset, it achieves a mean Intersection over Union (mIoU) of 0.740 with an inference time of 1.31 s. Cross-scene tests at two other locations, Shumuyuan and Langwang Village, yield mIoUs of 0.762 and 0.757, respectively. These results confirm robust performance even with limited annotations. Overall, our findings demonstrate the practicality of our approach for routine power line inspections, enabling reliable hazard detection with minimal annotation effort. Full article

Power line component inspection is essential for maintaining the reliability of the electrical power infrastructure. Recently, some researchers have studied automatic power line inspection using drones and deep learning. However, fully supervised deep learning approaches require large amounts of labeled data that are difficult and expensive to obtain in real-world environments. To address these challenges, this paper proposes an edge-optimized semi-supervised deep learning framework for power line component inspection. The proposed approach combines a semi-supervised learning (SSL) strategy to leverage both limited labeled images and abundant unlabeled field data with hardware–software (HW-SW) co-optimization techniques for efficient deployment on resource-constrained edge devices. In the learning stage, the framework improves detection performance by leveraging unlabeled inspection data via pseudo-labeling and confidence-based sample selection, thereby reducing annotation effort while maintaining robust recognition performance. In the deployment stage, the quantization technique was applied to enable real-time operation on embedded platforms with limited computational resources and power budgets. In this paper, an improved version of the edge-AI deployment score, the generalized edge-AI deployment score (GEADS), is proposed. In SSL evaluation, debiased semi-supervised learning (DeSSL) achieves a higher observed mAP@0.5 and F1-score than the standard SSL method in the single-run simulations using dataset 1 and dataset 2. In hardware evaluation, the YOLOv7-Tiny (INT8) configuration implemented on a Raspberry Pi 5 achieves the highest GEADS of 0.657, confirming it offers the most balanced performance among the required parameters. From the simulation, it is also confirmed that the proposed GEADS provides a more interpretable and statistically stable metric than the existing metric to evaluate the edge deployment. Full article

The essential role of electricity supply for public and private services highlights the need to monitor the stability of power transmission networks during, or immediately after, hazardous events. In the aftermath of calamities, traditional field inspections may be impractical or unsafe, leaving operators without timely information on the condition of critical assets. In this paper, we present and discuss the performance of two automatic Artificial Intelligence (AI)-based models (Multi-Layer Perceptron (MLP) neural network architectures and Support Vector Machine (SVM) model) designed to automatically assess the status of high-voltage transmission towers and power lines through multi-temporal spaceborne Synthetic Aperture Radar (SAR) image analysis. Model development and testing rely on real COSMO-SkyMed Stripmap observations of damaged towers and power lines affected by documented hazardous events across Italy, complemented by simulated tower data generated with a physics-guided, signature-based SAR simulator designed to preserve the observed target-to-background contrast and spatial footprint patterns of real SAR tower signatures. Results indicate that the MLP, trained on either real or simulated data, achieved 100% Overall Accuracy (OA) with no observed false positives or false negatives within the considered visibility-screened real test set, while providing inference times on the order of tenths of milliseconds per target… Computational performance characteristics, operational advantages, and the potential pathway toward satellite on-board porting are discussed to enhance situational awareness and support the prioritisation of interventions during critical events. Full article

Aiming at the core pain point that in existing object detection models, it is difficult to balance detection accuracy and real-time inference efficiency on edge computing devices in UAV-based intelligent inspection of power transmission lines, this paper proposes a lightweight YOLO-PowerLiteV2 model for anomaly target detection in power transmission lines to address the shortcomings of YOLO-PowerLite. Based on YOLO11n as the baseline, the model achieves compression of model volume while guaranteeing detection performance through four core improvements: the C3k2-UIB lightweight backbone module, the MCA (Multi-scale Cross-Axis) attention mechanism, the MBConv lightweight detection head, and the MFM (Modulation Feature Fusion) module. Experiments were conducted on a dataset constructed from 5563 aerial images of transmission lines containing three types of targets: bird nests, defective insulators, and balloons. The results show that YOLO-PowerLiteV2 achieves a mAP@50 of 95.2%, with only 0.97 M parameters and 2.8 G floating point operations (FLOPs). Compared with the baseline model, the number of parameters is reduced by 62.5%, and FLOPs are decreased by 56.25%. On the NVIDIA Jetson Xavier NX edge platform, the model achieves 59.5 FPS with only 16.8 ms latency, outperforming the baseline by 31% in frame rate. Its comprehensive performance outperforms mainstream lightweight detection models. The model demonstrates excellent adaptability to UAV edge-terminal deployment requirements, thereby providing technical support for real-time intelligent inspection of power transmission lines. Full article

The You Only Look Once (YOLO) series has consistently advanced the field of object detection, evolving from YOLOv1 to the latest YOLO26, achieving remarkable improvements in detection accuracy and computational efficiency. However, deploying such high-performance models on resource-constrained edge devices remains challenging, particularly for tasks requiring real-time inference. A critical yet often overlooked factor affecting edge deployment is the trade-off between input image resolution and computational cost: while higher resolution preserves fine-grained details essential for detecting small defects, it proportionally increases energy consumption and latency. To address this issue, we propose a novel multi-resolution adaptive detection framework based on YOLO26, specifically optimized for Ascend NPU edge devices. Our method dynamically selects the most suitable input resolution for each inference instance via a jointly optimized scene complexity metric, where the feature weights and resolution thresholds are simultaneously calibrated through Bayesian multi-objective optimization to achieve an optimal balance between predictive accuracy and energy efficiency. The experiments on transmission line insulator defect detection demonstrate that our approach achieves favorable trade-offs, maintaining high detection precision while significantly reducing power consumption compared to fixed-resolution baselines. The proposed framework provides a viable solution for intelligent visual inspection in power grid infrastructure. Full article

To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model’s feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model’s occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

Automated power line inspection plays a crucial role in maintaining grid reliability within smart cities by identifying potential defects in towers, conductors, insulators, and fittings. While modern anomaly detection relies heavily on deep neural networks (DNNs), training these models requires massive amounts of high-quality image data. However, a significant scarcity of publicly available datasets persists because data acquisition not only demands highly specialized professional skills but also faces strict data protection regulations enforced by grid companies. To bridge this gap, this paper presents a comprehensive review of open-access image datasets dedicated to power line inspection. Based on strict inclusion criteria—specifically, unrestricted public availability and a direct focus on core power line components—19 datasets are systematically selected and analyzed. We provide a detailed taxonomy and comparative analysis of these datasets in terms of inspection targets, acquisition platforms, annotation toolkits, and labeling schemes. Furthermore, our investigation highlights current research trends and identifies critical gaps, such as the disproportionate focus on insulators and the notable scarcity of multimodal data. To address the limitations of small-scale datasets, we also discuss existing data augmentation strategies and synthetic data generation techniques. Ultimately, this review serves as a unified navigational guide, aiming to foster the development of more robust visual inspection algorithms and to inspire future high-quality dataset construction in the power domain. Full article

Robotics optimization is essential for improving the performance, efficiency, and reliability of robotic systems, especially when dealing with complex engineering problems involving multiple conflicting objectives. Algorithm-specific parameter-free metaheuristic algorithms have gained attention in such applications because they eliminate the need for problem-specific parameter tuning. However, their performance can be further enhanced by improving convergence and maintaining solution diversity in multi-objective optimization. This paper proposes three multi-objective variants—archive, opposition, and self-adaptive multi-population (SAMP)—for the algorithm-specific parameter-free BxR algorithms such as Best–Mean–Random (BMR), Best–Worst–Random (BWR), and Best–Mean–Worst–Random (BMWR). The proposed variants are evaluated on five robotic optimization problems spanning two to six objectives, including Autonomous Underwater Vehicle shape optimization, power line inspection robot design, inverse kinematics of a 4-DOF manipulator, wall-building robot trajectory planning, and optimization of a reconfigurable parallel cutting and grinding mechanism. Their performance is compared with several established multi-objective algorithms using metrics such as GD, IGD, SPC, and HV, supported by rigorous statistical testing involving Friedman tests, Conover post hoc analysis with Holm correction, and Vargha–Delaney A₁₂ effect sizes over 30 independent runs. The results show that archive variants achieve the best IGD rank in four of the five case studies and the best HV rank in three of them, with the five-objective trajectory planning problem being the sole exception where SAMP and base BxR variants show improved IGD performance. The base BxR algorithms prove to be strong competitors, consistently outperforming established parameter-dependent methods on IGD across all five problems. The opposition variants do not provide consistent improvement; however, they also do not cause catastrophic degradation, suggesting that refined opposition strategies warrant further investigation. The study demonstrates the effectiveness of the proposed algorithms as practical optimization tools for complex robotic optimization problems. Full article

In unmanned aerial vehicle (UAV)-based power line inspection, multi-scale defects and complex backgrounds challenge the balance between detection accuracy, speed, and model lightweighting, limiting automated grid inspection. This paper proposes a Multi-Scale Mamba Framework (MS-Mamba) for efficient and accurate defect perception. A drone inspection dataset containing 5137 images from 14 defect categories was constructed and divided into training and validation sets with an 8:2 split. To address the large scale variation among defects, the categories are decoupled into macroscopic, mesoscopic, and microscopic groups according to physical attributes and visual scales. As the core perception engine, a lightweight state-space mechanism is designed to balance accuracy and deployability. A spatial resolution-aware hierarchical reconstruction strategy and a dynamic feature selection mechanism are integrated to enhance feature extraction, reduce background redundancy, and improve small-target representation. Compared with the YOLOv5s baseline, MS-Mamba achieves an mAP@0.5 of 0.749, corresponding to a 15.6 percentage-point improvement, while reducing parameters by 0.13 M and computational cost by 1.7 GFLOPs. Ablation studies and visual analyses further confirm fewer missed and false detections in complex backgrounds. The developed end-to-end inspection system was validated through closed-loop engineering tests, demonstrating strong potential for industrial deployment. Full article

We present a hybrid algorithm for 3D obstacle-avoidance path planning of a six-axis robotic arm in cable inspection environments. It improves on traditional RRT, which suffers from blind sampling and low efficiency, and APF, which tends to become stuck in local optima and has unstable potential fields. For the bidirectional RRT, we introduce target-biased sampling and a dynamic step-size expansion strategy driven by target attraction to enhance sampling directionality. For the APF, we optimize the potential field function by incorporating shape and size factors, use simulated annealing to overcome local optima, and apply Gaussian filtering to smooth the potential field. A triangular inequality pruning strategy with a target chain is then used to optimize the initial path, combined with cubic B-spline curves for path smoothing, and we design a simplified collision detection method to reduce computational cost. Simulation experiments are carried out in 2D and 3D spaces, as well as in a robotic arm setup that mimics cable inspection. Compared with basic RRT, bidirectional RRT, and the RRT-APF fusion algorithm, our method achieves significant improvements in average iteration count, planning time, path length, and number of generated nodes. The resulting trajectories are shorter and smoother, effectively boosting the efficiency and quality of 3D obstacle-avoidance path planning for six-axis robotic arms, and offering a practical solution for engineering scenarios such as power line inspection. Full article

To address the challenges of insufficient small target recognition, difficulty in edge information extraction, and high computational overhead in insulator defect detection, this paper proposes a lightweight detection method based on the YOLO-MLSL model for transmission line insulator defect detection. First, a C2f-LRFCA module is introduced, effectively enhancing feature interaction through a long-range convolutional attention mechanism, thereby improving the perception of fine-grained defects. Second, an MEUM multi-scale feature enhancement module is designed to achieve more efficient contextual information fusion during upsampling, improving the detection performance for multi-scale targets. Third, the ShapeIoU loss function is employed to improve the bounding box regression accuracy in complex backgrounds, and LAMP pruning technology significantly reduces the model’s computational and storage overhead. Experimental results show that the improved algorithm achieves an mAP@0.5 of 85.4%, a 4.1% improvement compared to the original YOLOv8n, while maintaining a low parameter count and computational complexity, demonstrating both high accuracy and efficiency. This research provides a valuable reference for the design and application of lightweight target detection models in the intelligent inspection of power equipment. Full article

Tungsten carbide and cobalt cutting tools require low surface roughness to improve cutting performance by reducing the wear from machining friction. While this is achieved by conventional manufacturing processes (pressing and sintering, grinding), with additive manufacturing processes it is more difficult (layer height, printing strategy). Since less costly and more sustainable solutions (without lubricants) are being studied as alternatives to conventional processes, a complementary technology (laser ablation) is suggested for the additive manufacturing of green WC-10Co. In this study, material extrusion (MEX) was used to produce green WC-10Co 3D objects, followed by laser ablation (50 W ytterbium fiber laser, 800–1100 nm wavelength) on their surface. Different laser strategies and parameters (power, speed, frequency, distance between lines, number of passages) were tested to find the most suitable. Most combinations were excluded by initial visual inspection, while the best ones were measured with a contact and non-contact profilometer. Further analysis was made on the composition and microstructure (with techniques such as Raman spectroscopy, scanning electron microscope, x-ray diffraction, and hardness indentation) to study what the interaction with the laser changed on the surface. Results show that with a combination of 50 W laser power, 1000 mm/s laser speed, 2000 kHz laser frequency, 0.1 mm distance between lines and three laser passages, it was possible to achieve a surface roughness of 0.6 µm (Sa) for the sintered WC-10Co, produced by MEX. No η-phase and graphite were detected, as well as microporosity and fissures. Full article

To address the challenges of strong coupling between resource allocation and collaborative scheduling in vehicle–UAV cooperative inspections of power distribution lines, as well as the difficulty in balancing efficiency and stability, this paper proposes a path-scheduling feedback-based evolutionary cooperative optimization method. First, an integrated modeling framework for resource allocation and execution scheduling is constructed, incorporating vehicle path decisions and drone task scheduling into a unified optimization space. Next, a feedback-driven two-layer multi-objective evolutionary collaborative optimization algorithm (FB-MOC²) is introduced. The outer layer performs evolutionary search for adaptive resource allocation, while the inner layer solves path planning and collaborative scheduling, with dynamic resource adjustments achieved through execution-layer feedback, forming a data-driven adaptive optimization process. Subsequently, sensitivity analysis is conducted on resource deployment mechanisms, revealing phased evolutionary patterns between resource scale and system performance, and identifying the effective operational range for resource allocation. Finally, the algorithm’s robustness is validated under multiple failure scenarios. Simulation results demonstrate that the proposed method reduces total operation time from 412 min to 315 min, improves battery utilization to 78.5%, and maintains recovery costs within 1.65 times the baseline even under high drone failure rates, while ensuring full inspection coverage. This approach provides an effective bio-inspired and data-driven solution for adaptive resource allocation and robust scheduling in intelligent power distribution line inspections. Full article