基于卷积神经网络的航拍视频轨道异物检测方法
俞军燕1黄皓冉2杨毅1邢宗义2
Aerial Video Detection Method of Track Abnormal Objects Based on Convolutional Neural Network
YU JunyanHUANG HaoranYANG YiXING Zongyi
-
作者信息:1.1.广州地铁集团有限公司， 510335， 广州；
2.2.南京理工大学自动化学院， 210094， 南京
-
Affiliation:Guangzhou Metro Group Co., Ltd., 510335, Guangzhou, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.2022.10.018
-
中图分类号/CLCN:U216.3
-
栏目/Col:研究报告
摘要:
针对无人机航拍视频轨道异物检测存在动态背景及异物类型多样等问题，提出了一种基于CNN(卷积神经网络)的航拍视频轨道异物检测方法:对航拍单帧图像采用Canny边缘检测、概率Hough变换、线段筛选等确定轨道区域；采用改进的MobileNet CNN模型对轨道区域图像进行单帧图像异物检测分类；利用视频的帧间相关性优化单帧检测结果，得到最终的视频轨道异物检测结果;并采用自建的实拍轨道区域图像数据集进行试验。结果表明，该方法适用于航拍视频中存在多种类型异物的情况，并能实现有效检测。
Abstracts:
Targeting the problems of dynamic background and diverse types of track abnormal objects in UAV (unmanned aerial vehicle) aorial vedio detection, an aerial vedio detection method of track abnormal objects based on CNN (convolutional neural network) is proposed. The track region in a single-frame image is affirmed by Canny edge detection, probabilistic Hough transform, and line segments screening. By adopting the improved MobileNet CNN model, abnormal objects detection and categorization in track region in a single-frame image is carried out. From the result of single-frame detection by the inter-frame correlation optimization in the video, the final results of the video track abnormal objects detection are obtained. Then, a self-built aerial video track region image dataset is used for test. Results demonstrate that the proposed method is suitable for multiple types of abnormal objects in aerial video, with capability of realizing effective detection.
- 上一篇: 高铁物流运输模式及其可行性
- 下一篇: 深圳都市圈城际铁路CBTC系统功能需求研究
过刊浏览
资料下载
友情链接