摘要
为了提高基于双目视觉的三维目标检测的精度与鲁棒性,提出了一种基于迭代式自主学习的三维目标检测算法。首先,为了给三维目标检测任务提供更精准的目标点云信息,提出了一种基于迭代式自主学习的视差估计算法,通过迭代地增加目标区域的视差监督信号以及引入选择性优化策略,提高了视差估计在目标区域中的准确性。其次,在网络结构中,提出了一种自适应特征融合机制,将不同模态信息的特征进行自适应融合,进而得到准确且稳定的目标检测结果。结果表明,与近年来较流行的基于视觉系统的算法相比,所提出的三维目标检测算法在检测精度上有较大提升。
To improve the precision and robustness of 3Dobject detection based on stereo vision,a novel 3D object detection algorithm based on iterative self-training is proposed.To acquire the precise object point clouds for 3D object detection task,a disparity estimation algorithm based on iterative self-training is first proposed,which is capable of improving the disparity accuracy of object region by increasing the supervised signal in object region iteratively and introducing a selective optimization strategy.Then a self-adaptive feature fusion mechanism is proposed in network architecture,which adaptively fuses the features from multimodal information to obtain the precise and robust object detection results.Compared with the recent and popular algorithms based on vision system,the proposed 3Dobject detection algorithm achieves a great improvement in precision.
作者
王康如
谭锦钢
杜量
陈利利
李嘉茂
张晓林
Wang Kangru;Tan Jingang;Du Liang;Chen Lili;Li Jiamao;Zhang Xiaolin(Bionic Vision System Laboratory,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China;University of Chinese Academy of Sciences,Beijing,100049,China;Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence,Ministry of Education,Institute of Science and Technology for Brain-Inspired Intelligence,Fudan University,Shanghai 200433,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2020年第9期127-139,共13页
Acta Optica Sinica
基金
国家自然科学基金(61806189)
上海市自然科学基金(17ZR1436000)
上海市市级科技重大专项(2018SHZDZX01)
上海市“科技创新行动计划”项目(17511105803)。
关键词
机器视觉
三维目标检测
立体视觉
卷积神经网络
自主学习
machine vision
3Dobject detection
stereo vision
convolutional neural network
self-training
