HCDFI-YOLOv8:基于复杂环境环境中改进的YOLOv8的传输线路冰盖检测模型
Lipeng Kang1, Feng Xing1, Tao Zhong2
1School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了改进的You Only Look Once版本8 (YOLOv8) 模型,用于使用无人机检测输电线路上的冰. 新的HCDFI-YOLOv8模型在复杂的环境中提高了准确性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 在传输线路冰层检测方面面临挑战,原因是角度可变和复杂的背景.
- 现有的方法存在冰覆盖识别精度差,目标识别困难等问题.
研究的目的:
- 为使用无人机的输电线路开发一个改进的结冰检测模型.
- 在具有挑战性的环境中提高冰盖检测的准确性和稳定性.
主要方法:
- 提出了一个改进的You Only Look Once版本8 (HCDFI-YOLOv8) 模型.
- 引入了一个交叉密度混合 (CDH) 平行异质卷积模块,以提高准确性和计算效率.
- 实现了深度和浅度特征加权融合与改进的CSPDarknet53到2阶段FPN_动态特征融合 (C2f_DFF) 模块,以减少特征损失.
- 优化了检测头,使用特征自适应空间特征融合 (FASFF) 模块进行多尺度特征提取.
- 开发了一个新的内部完整的交叉点对联 (Inner_CIoU) 损失函数,以解决原来的CIOU损失的限制.
主要成果:
- 与标准YOLOv8.8相比,HCDFI-YOLOv8模型实现了mAP@0.5的2.7%改善和mAP@0.5:0.95的2.5%改善.
- 在12个评估的冰层检测模型中,证明了总体检测准确度的优越性.
- 在复杂的输电线路环境中验证了模型的有效性.
结论:
- 拟议的HCDFI-YOLOv8模型显著提高了输电线路冰盖检测准确度.
- 该模型为在具有挑战性的条件下基于无人机的冰检测提供了有效的技术支持.
- 卷积模块,特征融合和损失功能的创新有助于提高性能.
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