摘要
为提高航拍图像中输电线路绝缘子的检测准确性,提出一种改进的Faster RCNN网络模型。在原始Faster RCNN网络模型上运用多尺度训练,同时根据绝缘子自身特性调整滑动窗口产生的候选区域比例,并引入检测困难样本的对手生成策略,实现不同尺寸及部分遮挡输电线路绝缘子的准确检测。实验结果表明,改进的Faster RCNN网络模型相比原始Faster RCNN网络模型的检测精确度提升了4.33个百分点,能更准确地检测出目标绝缘子。
To improve the detection accuracy of transmission line insulators in aerial images,this paper proposes an improved Faster RCNN model.The method applies multi-scale training to the original Faster RCNN model,and adjusts the proportion of the candidate regions generated by sliding window according to the characteristics of insulators.In addition,an opponent generation strategy for detecting difficult samples is introduced to realize accurate detection of insulators for different sizes and partial shielded transmission lines.Experimental results show that the accuracy of the improved Faster RCNN model is 4.33 percentage points higher than that of the original Faster RCNN model,proving that the model can detect the target insulators more accurately.
作者
易继禹
陈慈发
龚国强
YI Jiyu;CHEN Cifa;GONG Guoqiang(School of Computer and Information,China Three Gorges University,Yichang,Hubei 443000,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2021年第6期292-298,304,共8页
Computer Engineering
基金
国家重点研发计划(2016YFB0800403)。
关键词
电力巡检
绝缘子
目标检测
多尺度训练
对手生成策略
power inspection
insulator
object detection
multiscale training
opponent generation strategy