Electric and Magnetic Fields Analysis of the Safety Distance for UAV Inspection around Extra-High Voltage Transmission Lines

Boukabou, Issam; Kaabouch, Naima

doi:10.3390/drones8020047

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…The model training parameters were configured as follows: the maximum number of iterations was set to 500, utilizing stochastic gradient descent (SGD) [36] as the optimizer with a momentum value of 0.9. The learning rate adjustment strategy [37] employed cosine annealing decay, as outlined in Equation (19).…”

Section: Network Trainingmentioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

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The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

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Section: Network Trainingmentioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

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Impact of Aircraft Wake Vortex on Unmanned Aerial Systems near Powerlines

Benouadah,

Foerster,

Mullins

et al. 2024

2024 Integrated Communications, Navigation and Surveillance Conference (ICNS)

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Research and Design of Grid Safety Inspection Path Planning and Work Order Assignment Based on Dijkstra and Optimal Scheduling

Liu,

Zhou,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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The power grid is the core of power energy transmission and distribution in modern society and is affected by many factors, such as the number and variety of its equipment and the intricate structure of the grid, which leads to a more severe situation that the safe operation of the power grid will face. This paper gathers data on the operational status of grid equipment, historical maintenance records, customer complaints, and more. It then employs Monte Carlo simulation sampling to select and analyze the risk state of the grid system, as well as to determine the overall risk probability of the calculation system and the level of consequence indicators. An improved Dijkstra algorithm is applied to plan the optimal path for UAV grid inspection. Based on the inspection and risk assessment results, work orders are automatically generated and assigned to the corresponding maintenance personnel. Simulation results show that the proposed method obtains two optimal inspection schemes, TSP and EETSP. The TSP scheme obtains the shortest path when the inspection data volume is 50 Mbits, and the EETSP scheme obtains the shortest moving time when the inspection data volume is 200 Mbits. The UAV-cooperative transmission line inspection path planning method proposed in this paper can improve grid inspection efficiency and effectively solve the optimal path planning problem in large inspection areas.

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Electric and Magnetic Fields Analysis of the Safety Distance for UAV Inspection around Extra-High Voltage Transmission Lines

Cited by 9 publications

References 14 publications

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Impact of Aircraft Wake Vortex on Unmanned Aerial Systems near Powerlines

Research and Design of Grid Safety Inspection Path Planning and Work Order Assignment Based on Dijkstra and Optimal Scheduling

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