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
唐勋昊 1樊绍胜1
作者信息
- 1. 长沙理工大学电气与信息工程学院,湖南 长沙 410111;电力机器人湖南省重点实验室,湖南 长沙 410111
- 折叠
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.
Abstract
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Key words
motion blurred image restoration/conditional generative adversarial network/YOLOv8/small target detection for transmission lines/multi-head self-attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据