激光与光电子学进展, 2020, 57 (4): 041509, 网络出版: 2020-02-20   

面向无人机飞控平台的实时道路目标深度神经网络检测方法 下载: 1024次

Method of Real-Time Road Target Depth Neural Network Detection for UAV Flight Control Platform
作者单位
西安科技大学机械工程学院, 陕西 西安 710054
摘要
针对现有目标深度神经网络检测准确度低和实时性差的问题,提出一种面向无人机(UAV)飞控平台的实时道路目标深度神经网络检测方法。该方法结合了YOLOv2和YOLOv3网络的优点,针对YOLOv2检测准确度低、小目标难以检测和YOLOv3实时性差的现状,建立了将残差块引入Darknet-19网络同时采用多尺度特征进行目标检测的道路目标检测模型。提出采用回归(logistic)分类器进行目标类别的预测,以实现对重叠图像的多标签分类。实验结果表明,该方法对UAV飞控平台上分辨率为416 pixel×416 pixel视频图像的检测帧率在20 frames/s以上,平均准确度(mAP)达到82.29%,召回率达到86.7%,基本满足UAV飞控平台道路目标检测对准确性和实时性的要求。
Abstract
In view of low accuracy and poor real-time performance of the existing target detection methods, a real-time road target detection method based on depth neural network on the unmanned aerial vehicle(UAV) flight control platform is proposed. The method combines the advantages of YOLOv2 and YOLOv3 networks, and proposes a model of object detection which introduces the Darknet-19 network with residual block and multi-scale features, considering the current situation that YOLOv2 has a low accuracy of road target detection and is difficult to detect small target, and YOLOv3 has a poor real-time performance. The regression classifier is proposed to achieve multi-label classification of overlapping images. The experimental results show that the proposed method has a detection frame rate of 20 frames/s or more on the UAV flight control platform for the video image with a resolution of 416 pixel×416 pixel, the mAP reaches 82.29%, and the recall rate reaches 86.7%, basically meets the requirements of road target detection accuracy and real-time performance on the UAV flight control platform.

黄涛, 赵栓峰, 拜云瑞, 耿龙龙. 面向无人机飞控平台的实时道路目标深度神经网络检测方法[J]. 激光与光电子学进展, 2020, 57(4): 041509. Tao Huang, Shuanfeng Zhao, Yunrui Bai, Longlong Geng. Method of Real-Time Road Target Depth Neural Network Detection for UAV Flight Control Platform[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041509.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!