摘要
针对三维不规则的点云格式和密度不均匀的问题,提出了一种融合多注意力机制与PointRCNN网络用于三维点云目标检测。本实验主要对PointRCNN两阶段网络分别进行改进,首先,把通道注意力与空间注意力机制串行通过调节输入到第一阶段各网络层的分布,批量归一化进一步快速识别三维特征;其次,引入交叉位置注意力机制到第二阶段网络为了避免交叉路径出现位置偏差,从而进一步精细化三维目标位置以进行特征提取。在KITTI数据集上实验结果表明:相比于PointRCNN检测网络,改进的网络在小汽车和行人测试上平均均值精度(mAP)分别提高了1.2%、1.9%。因此改进的方法在解决了点云格式不规则和密度不均匀问题的同时还保证了检测精度。
Aiming at the problem of 3 D irregular point cloud format and uneven density, a fusion of multi attention mechanism and pointrcnn network is proposed for 3 D point cloud target detection. This experiment mainly improves the pointrcnn two-stage network respectively. Firstly, the channel attention and spatial attention mechanism are serially input to the distribution of each network layer in the first stage by adjusting and normalizing in batch to further quickly identify three-dimensional features;Secondly, the cross position attention mechanism is introduced into the second stage network to avoid the position deviation of the cross path, so as to further refine the three-dimensional target position for feature extraction. The experimental results on Kitti data set show that compared with pointrcnn detection network, the improved network improves the average mean accuracy(map) of car and pedestrian tests by 1.2% and 1.9% respectively. Therefore, the improved method not only solves the problems of irregular point cloud format and uneven density, but also ensures the detection accuracy.
作者
郑美琳
高建瓴
Zheng Meilin;Gao Jianling(College of Big Data&Information Engineering,Guizhou University,Guiyang 550025,China)
出处
《电子测量技术》
北大核心
2022年第9期127-132,共6页
Electronic Measurement Technology
基金
国家自然科学基金(62062021,61872034)
贵州省科学技术基金(黔科合基础[2020]1Y254)
贵州省自然科学基金(黔科合基础[2019]1064)项目资助。
