基于YOLOv8n改进的航拍输电线路图像多类电力部件检测算法

An improved algorithm for detecting components of power transmissionlines from aerial inspection images

YOLO系列算法已在电力巡检航拍图像处理中得到广泛应用。但使用无人机进行电力巡检航拍所获得的图像通常包含大量且密集的小尺寸电力部件目标,直接利用YOLO算法难以实时、高效地将其从复杂背景中检测出来。本文对YOLOv8n进行轻量化改进,旨在提高电力部件识别精度和速度。引入坐标注意力机制进行特征学习,充分利用目标的位置信息,提高网络对于背景和目标的区分能力,以提升遮挡目标、小目标的检测精度;将可变形卷积融入C2f模块中,使算法能根据不同尺度的特征自适应调整感受野的大小和形状,获得全局兼局部的特征表示;在主干网络引入GSConv模块,减少模型参数量以提高检测速度。试验结果表明,相对于YOLOv8n,本文改进方法的识别精度、速度均有提升,mAP50提升了5.7%,F1值提升了3.4%,模型参数量降低了7.0%,浮点计算量降低了9.8%,检测速度达到107.5帧/s,提升了3.1%,满足输电线路部件检测的精度、轻量化与实时性等要求。

The YOLO algorithm has been widely applied to process images obtained by aerial inspection of power transmission lines.However,these UAV images often contain a large number of dense small-sized power component targets.It is difficult to detect these targets in real-time and efficiently from complex backgrounds using the YOLO algorithm alone.In this paper,we make some lightweight improvements to YOLOv8n to enhance the accuracy and speed of power component recognition.Deformable convolution sare inserted into the C2f modules of the YOLO backbone and neck,to make our method adaptively adjust the size and shape of the receptive field based on features of different scales,and obtain global and local feature representation.GSConv convolutions are integrated into the YOLO backbone,to reduce the number of model parameters and improve the detection speed.Experimental results demonstrate that our proposed method improves the recognition accuracy and speed compared to YOLOv8n,and meets the requirements of precision,lightweight,and real-time inspection of transmission line components.Specifically,the mAP50 is improved by 5.7%,the F1-score is improved by 6.0%,the number of model parameters is reduced by 7%,and the detection speed reaches 107.5 fps.