激光与光电子学进展, 2019, 56 (8): 081006, 网络出版: 2019-07-26
基于视觉导航的输电线杆塔方位确定方法 下载: 1276次
Methodfor Orientation Determination of Transmission Line Tower Based on Visual Navigation
图像处理 视觉导航 梯度方向直方图特征 多层感知机 杆塔检测 神经网络 image processing visual navigation histogram of the gradient (HOG) feature multi-layer perceptron tower detection neural network
摘要
通过分析杆塔镂空的结构特征,提出了一种基于杆塔梯度方向直方图(HOG)的由远及近杆塔部件检测方法。使用不同方位下杆塔HOG特征训练多层感知机(MLP),得到训练后的分类模型,将航拍图像输入到分类模型中识别杆塔的方位,最终实现了局部目标的检测。相比于深度学习神经网络,该方法的分类特征更加明确,更具有代表性。实验结果表明,所提方法的检测准确率比Faster RCNN(Regions with Convolutional Neural Networks)方法高27.9%,运算时间比Faster RCNN减少70.6%。所提方法适用于在开阔环境下利用无人机对杆塔方位及其局部部件的精确检测。
Abstract
A method for detecting the tower components from far to near is proposed based on the histogram of the gradient (HOG) along the tower gradient direction by analyzing the structural characteristics of hollowing-out of a tower. The multi-layer perceptron (MLP) is first trained by using the HOG feature of the tower under different orientations to obtain a trained classification model, then the aerial image is input into this classification model to identify the orientation of the tower, and the detection of a local target is finally realized. Compared with that of a deep learning neural network, the classification feature of the proposed method is clear and representative. The experimental results show that the detection accuracy of the proposed method is 27.9% higher than that of the Faster RCNN (Regions with Convolutional Neural Networks) method, but the computation time is 70.9% lower than the latter. The proposed method is suitable for the accurate detection of the tower orientation and its local parts by the unmanned aerial vehicle in an open environment.
王祖武, 韩军, 孙晓斌, 杨波. 基于视觉导航的输电线杆塔方位确定方法[J]. 激光与光电子学进展, 2019, 56(8): 081006. Zuwu Wang, Jun Han, Xiaobin Sun, Bo Yang. Methodfor Orientation Determination of Transmission Line Tower Based on Visual Navigation[J]. Laser & Optoelectronics Progress, 2019, 56(8): 081006.