首页|基于Ghost改进的YOLOv5轻量化双目视觉无人机避障算法

基于Ghost改进的YOLOv5轻量化双目视觉无人机避障算法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
为解决无人机在室外实际飞行时的自主避障问题,提出一种基于Ghost改进的YOLOv5轻量化双目视觉无人机避障算法.首先,引入Ghost模块改进YOLOv5中的CBL和CSP_X单元,使用CIOUloss作为回归损失函数,并将非极大值抑制CIOUnms修改为DIOUnms以优化损失函数;其次,对双目相机进行标定和校正;使用ORB特征点提取和滑动窗口匹配算法得到检测目标的视差值,再根据视差值和相机内参求解出障碍物的距离信息;最后,根据障碍物的位置和距离实现无人机的自主避障.该避障算法在嵌入式系统中运行的平均FPS达到14.3,并用无人机避障飞行试验证实了该算法的可行性;改进后的网络检测平均准确率为76.88%,与YOLOv5相比,平均检测精度均值下降0.37%,但检测时间下降22%,参数量下降25%.该算法对无人机的自主避障具有重要的应用价值.
Improved YOLOv5 lightweight binocular vision UAV obstacle avoidance algorithm based on Ghost module
To address the issue of autonomous obstacle avoidance during unmanned aerial vehicle(UAV)flight in outdoor environments,a lightweight binocular vision-based UAV obstacle avoidance algorithm was proposed utilizing Ghost module to improve YOLOv5.Firstly,the Ghost module was introduced to enhance the CBL and CSP_X units of YOLOv5,while utilizing CIOUloss as the regression loss function,and optimizing the loss function by modifying the non-maximum suppression from CIOUnms to DIOUnms.Secondly,the stereo cameras were calibrated and corrected,and the ORB feature point extraction and sliding window matching algorithm was utilized to obtain the disparity value of the detected targets,and the distance information of the obstacle was solved based on the disparity value and camera intrinsic parameters.Finally,autonomous obstacle avoidance of the UAV was achieved based on the position and distance of the obstacle.The obstacle avoidance algorithm was implemented on an embedded system,an average FPS of 14.3 was achieved,and the feasibility of the algorithm was verified through UAV flight testing.The improved network had an average detection accuracy of 76.88%,which was 0.37%lower than that of YOLOv5,but the detection time and parameter quantity were reduced by 22%and 25%,respectively.This algorithm has significant value for the autonomous obstacle avoidance of UAVs.

object detectionlightweightfeature matchingobstacle avoidance unmanned aerial vehicles

贾一凡、曹天一、白越

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中国科学院 长春光学精密机械与物理研究所,吉林 长春 130033

中国科学院大学,北京 100049

西南交通大学-利兹学院,四川 成都 610097

目标检测 轻量化 特征匹配 无人机避障

国家自然科学基金国家自然科学基金吉林省科技发展计划重点项目吉林省科技发展计划重点项目省院合作科技专项中国科学院青年创新促进会项目中国科学院轻型动力创新院重点基金

113723096130401720150204074GX20160204010NY2020SYHZ00312014192CXYJJ20-ZD-03

2024

10.37188/CJLCD.2023-0069

液晶与显示
中科院长春光学精密机械与物理研究所 中国光学光电子行业协会液晶分会 中国物理学会液晶分会

液晶与显示

CSTPCD北大核心
影响因子：0.964
ISSN：1007-2780
年,卷(期)：2024.39(1)
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