基于深度学习的多级跨域异物检测在电力传输线路上的研究
Qingxue Liu1,2, Xia Wang3, Yun Su1,2
1School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
一个新的深度学习模型CO-YOLO通过改进检测小目标或封闭目标来加强输电线路安全监测. 这种先进的模型为电网安全应用提供了卓越的准确性和效率.
科学领域:
- 电气工程
- 计算机视觉
- 人工智能
背景情况:
- 深度学习对于电网安全监测至关重要,特别是检测输电线路的危险.
- 现有模型面临复杂架构和检测小或封闭目标的挑战,阻碍实时和边缘设备应用.
研究的目的:
- 开发一个高效准确的深度学习模型,用于检测输电线路中的潜在安全隐患.
- 解决现有模型在检测小或封闭目标和边缘设备部署方面的局限性.
主要方法:
- 将YOLOv11模型与ConvNeXt网络集成,以创建ConvNeXt"你只看一次" (CO-YOLO) 模型.
- 使用贝叶斯优化进行超参数调整以加速模型的融合.
主要成果:
- 在IOU值0.5时,CO-YOLO的平均精度为98.4%,在IOU值0.5:0.95时为66.1%.
- 该模型的每秒率为303,在精度和效率上都超过了YOLOv11和ETLSH-YOLO.
- 与原始YOLOv11模型相比,CO- YOLO显示了mAP@0. 5的1. 9%改善和mAP@0. 5: 0. 95的2. 2%改善.
结论:
- 拟议的CO-YOLO模型显著提高了传输线安全危险检测的准确性和效率.
- CO-YOLO是电网安全应用中实时监控和边缘设备部署的有希望的解决方案.
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