华中科技大学学报（自然科学版）2023，Vol.51Issue(12)：8-13,6.DOI:10.13245/j.hust.238897
基于YOLO-6D改进的目标姿态估计算法
Improved target pose estimation algorithm based on YOLO-6D
摘要
Abstract
Aiming at the traditional attitude estimation algorithm's weak anti-background interference ability and poor recognition accuracy of occluded targets,deep learning was combined to propose an improved target attitude estimation model based on the YOLO 6D algorithm.The YOLO V2 detection network in the original algorithm was changed to the YOLO V3 network,and an attention mechanism was added to enhance the model's ability to detect objects with complex backgrounds and occlusions.The pose estimation method was adjusted to improve the estimation accuracy by selecting the cell group for EPnP pose estimation based on random sample consensus(RANSAC)algorithm,which was trained on LineMod dataset and tested on Occlusion LineMod dataset.According to the 2D projection index,when the distance threshold is 30 pixels,the accuracy of the algorithm in this paper is 72.30％under the Occlusion LineMod dataset.It runs at 25 frame/s on GTX2080Ti GPU with real-time processing capabilities.Its comprehensive performance exceeds other convolutional neural network(CNN)-based algorithms.关键词
深度学习/目标检测/姿态估计/卷积网络/注意力机制Key words
deep learning/target detection/pose estimation/convolutional network/attention mechanism分类
信息技术与安全科学引用本文复制引用
丛明,张犇,杜宇,李金钟..基于YOLO-6D改进的目标姿态估计算法[J].华中科技大学学报（自然科学版）,2023,51(12):8-13,6.基金项目
辽宁省中央引导地方科技发展专项项目(2021JH6/10500144). （2021JH6/10500144）
