在农业无人机场景中轻量级电源线视觉检测,基于改进的YOLOv12n模型
Yi-Tong Ge1, Bao-Ju Wang1, Shuai Sun1
1Academy of Ecological Unmanned Farm, College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了改进的YOLOv12n模型用于农业无人机电线检测,提高了准确性和速度,同时降低了计算成本. 该模型与现有方法相比显示出更高的性能,支持智能农业操作.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 农业技术 农业技术
背景情况:
- 自主无人飞行器 (UAV) 需要精确的电力线检测才能安全运行.
- 现有的电源线检测模型面临的挑战是准确性,推断速度和计算需求.
研究的目的:
- 开发一个改进的物体检测模型,用于在农业无人机应用中高效准确地检测电力线.
- 通过整合新的架构组件来提高YOLOv12n的性能.
主要方法:
- 使用现实世界的图像和TTPLA数据集创建了一个新的电力线数据集.
- 通过EfficientNetV2骨干,动态蛇形卷积,多尺度交叉轴注意力和专家混合 (MoE) 层来增强YOLOv12n模型.
- 改进的模型保留了区域注意力分区和残余高效层聚合网络.
主要成果:
- 增强型号实现了75.5%的mAP0.5,超过了YOLOv8n,YOLOv11n,YOLOv5n和Line-YOLO的表现.
- 它显示了参数 (80.07%),计算 (43.07%) 和重量大小 (77.35%) 的显著减少.
- 在移动端测试中,推断速度达到88.36 FPS,在实验模型中显示出最高的速度.
结论:
- 改进的模型为农业无人机电力线检测提供了卓越的概括性,稳定性,检测准确性和处理速度.
- 它为推进自主和智能农业操作提供了必要的技术支持.
- 该模型非常适合在电力线路检查任务中实际部署.
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