摘要
舰载机位姿实时检测对于甲板上的舰载机的运动控制、轨迹规划与防撞等具有重要意义。传统的舰载机调度主要依靠人工判断舰载机位置与航向角进行调度,传统方法不能得出准确数据,还易因为操作员的疏忽与疲劳发生碰撞事故。针对该问题,提出了舰载机位姿实时视觉测量算法。基于YOLO-V4(you only look once version 4)网络以及Canny边缘提取算法对舰载机进行识别分割。创新性地提出一种线框模板匹配算法,通过计算舰载机边缘轮廓与线框模板的匹配度获取最佳位姿。通过并行化与GPU(graphics processing unit)加速,使其满足实时性要求,并在1∶70与1∶14的实物模拟环境中完成测试。结果表明,该算法识别率在95%以上,位置精度在8 mm以内,姿态精度在0.7°以内,速度可达8 Hz。
Real-time detection of carrier aircraft pose is of great significance to the movement control,trajectory planning,and collision avoidance of carrier-based aircraft on deck.The traditional scheduling of carrier aircraft mainly relies on the manual judgment of the position and heading angle of the carrier-based aircraft.Traditional methods fail to obtain accurate data and readily cause collisions because of operator negligence and fatigue.Therefore,a real-time visual measurement algorithm for the pose of carrier aircraft is proposed.First,the carrier aircraft is identified and segmented based on the you only look once version 4(YOLO-V4)network and the Canny edge extraction algorithm.Then,an innovative algorithm for wireframe template matching is proposed,and the best pose is obtained by calculating the matching degree between the contour of the carrier aircraft and the wireframe template.The algorithm meets the real-time requirements through parallelization and graphics processing unit acceleration,and the test is completed in the 1:70 and 1:14 physical simulation environments.The results show that the recognition rate of this algorithm is>95%,the accuracy is within 0.7°and 8 mm,and the speed can reach 8 Hz.
作者
朱齐丹
李小铜
郑天昊
ZHU Qidan;LI Xiaotong;ZHENG Tianhao(College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《智能系统学报》
CSCD
北大核心
2021年第6期1045-1055,共11页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(61803116)
船舶态势智能感知系统研制项目(MC-201920-X01)。
关键词
机器视觉
舰载机位姿
深度学习
目标检测
边缘提取
线框匹配
GPU加速
目标分割
machine vision
shipborne aircraft pose
deep learning
target detection
edge extraction
wireframe matching
GPU acceleration
target segmentation
