摘要
随着目标检测技术在电力巡检任务中的不断推广,对电力巡检采集的各类图像进行自动分析已成为当前电力企业研究的热点方向之一。传统目标检测方法大多建立在机器学习技术之上,在复杂场景下的检测精度有待进一步提高,而基于图像的深度学习方法由于具有理想的检测精度及环境适应性被广泛应用于电力巡检目标检测。针对电力巡检复杂场景下采集到的图像质量差、背景复杂、对比度差等问题,提出了融合图像方向梯度直方图的区域卷积神经网络(HOG-RCNN)的红外图像目标检测方法,在图像进入RCNN网络之前对输入图像进行HOG特征提取,辅助RCNN实现候选区域的选取。算法实验表明,所提方法的检测效果优于单独的RCNN网络。
With the continuous promotion of object detection technology in electric power inspection tasks,automatic analysis of various images collected by electric power inspection has become one of the current hot research directions in electric power enterprises.Traditional target detection methods are mostly based on machine learning,and the detection accuracy in complex scenes needs to be further improved.The image-based deep learning method is widely used in power inspection target detection due to its ideal detection accuracy and environmental adaptability.Aiming at the problems of poor image quality,complex background,poor contrast,etc.collected in complex scenes of electric power inspection,an infrared image object detection method based on regional convolutional neural network fused with histogram of image orientation gradients(HOG-RCNN)was proposed.Before the image enters the RCNN,HOG feature extraction was performed on the input image for helping RCNN to select candidate regions.Algorithm experiments show that the detection effect of the method proposed is better than that of a separate RCNN network.
作者
魏豪
张凯
郑磊
曹源
张丁文
Wei Hao;Zhang Kai;Zheng Lei;Cao Yuan;Zhang Dingwen(State Grid Jilin Electric Power Co.,Ltd.,Changchun 130021,China;Information and Communication Company of State Grid Jilin Electric Power Co.,Ltd.,Changchun 130000,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2020年第S02期242-247,共6页
Infrared and Laser Engineering
基金
国网科技项目“省级电网数据资产盘点关键技术研究及应用”(SGJLXT00JFJS2000098)
关键词
目标检测
方向梯度直方图
区域卷积神经网络
电力巡检
object detection
histogram of oriented gradient
region based convolutional neural networks
power inspection
