School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu Province, P. R. China.
PLoS One. 2024 Oct 7;19(10):e0311278. doi: 10.1371/journal.pone.0311278. eCollection 2024.
This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. First, simulation experiments and model analysis determine the keyframe and flight trajectory. Second, an improved FEC model is proposed, extracting features from aerial images in large ranges in real time and accurately identifying and classifying electric power inspection targets. In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. In conclusion, the proposed model improves the accuracy and scope of inspection, provides a more scientific strategy for electric power inspection, and ensures inspection efficiency.
本研究旨在通过使用特征嵌入卷积（FEC）模型改进直升机电力巡检流程，以解决小范围和实时性差的问题。首先，通过仿真实验和模型分析确定关键帧和飞行轨迹。其次，提出了一种改进的 FEC 模型，可从大范围内实时提取航拍图像的特征，并准确识别和分类电力巡检目标。在仿真实验中，与传统算法相比，该模型在电力电路和设备检测方面的准确率提高了 30%，检测范围扩大了 26%。此外，本研究还进一步利用强化学习技术对模型进行了优化，对不同的飞行环境和设施进行了对比分析，揭示了巡检目标的多样性和复杂性。优化模型在故障检测方面的性能提高了 36%以上。总之，所提出的模型提高了巡检的准确性和范围，为电力巡检提供了更科学的策略，保证了巡检效率。
