TFD-YOLOv8:一种用于输电线路的异物检测方法

TFD-YOLOv8:a transmission line foreign object detection method

基于无人机航拍图像的异物检测是输电线路智能巡检中的重要环节。YOLO目标检测算法精度高、速度快,是目前的主流算法。但在进行输电线路异物检测时,由于异物目标尺度多变、特征不显著,易出现误检、漏检等问题,提出一种用于输电线路异物检测的YOLOv8模型(TFD-YOLOv8)。首先,在YOLOv8颈部网络构建双分支下采样模块,截留下采样过程中易丢失的尺度相关细节信息,实现语义信息和细节信息的高效融合,提升不同尺度特征图的信息一致性。然后,在主干网络插入混合增强注意力模块,同时提取图像的全局和局部特征,分别生成空间注意力和通道注意力,得到一个包含局部信息、全局信息、空间信息和通道信息的混合增强注意力,增强网络对目标关键特征的捕捉能力。实验结果表明,与基线模型相比,本文方法的平均检测精度提升了6.7%,准确率和召回率分别提升了12.9%和5.1%,与多个现有目标检测方法相比,该方法在检测精度和复杂度上均具有优势。

Foreign object detection based on UAV aerial images is an important aspect for intelligent inspection of transmission lines.The YOLO target detection algorithm is high in accuracy and speed,making it the current mainstream algorithm.However,when carrying out transmission line foreign object detection,due to the variable scale and insignificant features of foreign object targets,problems such as misdetection and omission detection would arise.A YOLOv8 model(transmission line foreign detection-YOLOv8,TFD-YOLOv8)was proposed for transmission line foreign object detection.A two-branch downsampling module was constructed in the YOLOv8s neck network to intercept the scale-related detail information easily lost during the downsampling process,achieving the efficient fusion of semantic and detail information and improving the information consistency of feature maps at different scales.Then,a mix-enhancement attention module was inserted into the backbone network to simultaneously extract global and local features of the image,generating spatial attention and channel attention,respectively,and resulting in a mix-enhancement attention including local,global,spatial,and channel information.This enhanced the network’s ability to capture the key features of the targe.The experimental results showed that compared with the baseline model,the proposed method improved the average detection accuracy by 6.7%,and the accuracy and recall by 12.9%and 5.1%,respectively.This method demonstrated advantages in terms of detection accuracy and complexity compared with several existing target detection methods.