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融合注意力机制的电力巡检目标检测模型研究
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Abstract:
针对传统电力电网设备巡检方法存在的巡检效率低和安全隐患多等问题，提出了一种基于人工智能技术的无人巡检方法。通过引入Swin Transformer模型，优化了目标检测算法，提高了巡检的精度和实时性。首先，分析了电力电网设备巡检的现状和传统方法的局限性，并对无人巡检系统的实际应用挑战和未来发展趋势进行了探讨，提出了以深度学习、计算机视觉为核心的技术框架。采用图像增强技术扩充了数据集，并手动标注获取了高质量数据集。将Faster RCNN与Swin Transformer结合，应用于自制数据集，实现了高效稳定的目标检测。与传统方法相比显著提升了巡检效率，降低了漏检率和误检率。本研究成果为电力行业的数字化转型和升级提供了理论和实践价值。
A unmanned inspection method based on artificial intelligence technology is proposed to address the problems of low inspection efficiency and multiple safety hazards in traditional inspection methods for power grid equipment. By introducing the Swin Transformer model, the object detection algorithm has been optimized, improving the accuracy and real-time performance of inspections. Firstly, the current situation of power grid equipment inspection and the limitations of traditional methods were analyzed, and the practical application challenges and future development trends of unmanned inspection systems were discussed. A technical framework centered on deep learning and computer vision was proposed. We expanded the dataset using image enhancement technology and manually annotated it to obtain high-quality datasets. The improved Faster RCNN was combined with Swin Transformer and applied to self-made datasets to achieve efficient and stable object detection. Compared with traditional methods, it significantly improves inspection efficiency, reduces missed detection rates and false detection rates. The results of this study provide theoretical and practical value for the digital transformation and upgrading of the power industry.
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