基于运动模糊图像复原的输电线路小目标检测算法  

Small-Target Detection Method for Transmission Lines Based on Motion Blurred Image Restoration

作　　者：唐勋昊 樊绍胜[1,2] Tang Xunhao;Fan Shaosheng(School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410111,Hunan,China;Hunan Provincial Key Laboratory of Electric Power Robotics,Changsha 410111,Hunan,China)

机构地区：[1]长沙理工大学电气与信息工程学院,湖南长沙410111 [2]电力机器人湖南省重点实验室,湖南长沙410111

出　　处：《激光与光电子学进展》2024年第22期160-171,共12页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62271087);湖南省研究生科研创新项目(CX20220857)。

摘　　要：针对无人机巡检采集图像过程中受到气流扰动、旋翼振动、相对运动等因素影响,输电线路小目标图像易出现运动模糊,导致纹理细节丢失从而难以检测的问题,提出一种基于运动模糊图像复原的输电线路小目标检测算法。使用条件生成对抗网络(ViT-GAN)进行小目标运动模糊图像复原,强化其特征提取主干对图像全局和区域上的信息感知能力,提升图像复原质量便于后续进行目标检测;通过引入多头自注意力机制、增加小目标检测层并优化边界框损失函数改进YOLOv8网络,提升网络在背景复杂、目标尺度变化大的输电线路环境下对小目标的检测能力。实验结果表明,所提算法能准确检测输电线路小目标,线上6类小目标的平均查准率为92.77%,平均查全率为94.19%,平均F1-score为94.94%,漏检和误检情况得到改善,表现出较强的准确性和鲁棒性。Small-target images of transmission lines are prone to motion blur owing to factors such as air disturbance,rotor vibration,and relative motion during unmanned-aerial-vehicle inspections.This blurring leads to the loss of texture details,rendering small-target detection difficult.To address this problem,this study proposes a method for detecting small targets in transmission lines based on motion-blurred-image restoration.The proposed method utilizes a conditional vision Transformer-based generative adversarial network(ViT-GAN)to restore motion-blurred images of small targets,thereby enhancing the involved feature-extraction backbone’s ability to perceive the global and regional information in images and improving the quality of image restoration for subsequent object detection.The involved YOLOv8 network is enhanced by introducing a multi-head self-attention mechanism,adding a small-target detection layer,and optimizing the boundary-frame-loss function.This helps to achieve good small-target detection in transmission line environments with complex backgrounds and large target-scale variations.Experimental results demonstrate that the proposed method can be used for accurate small-target detection for transmission lines.The average recognition accuracy of six categories of small targets is 92.77%,with an average recall rate of 94.19%,and average F1-score of 94.94%.Overall,the proposed method effectively mitigates the problem of missing and false detection,demonstrating its high accuracy and robustness.

关 键 词：运动模糊图像复原 条件生成对抗网络 YOLOv8 输电线路小目标检测 多头自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]