基于FVOIRGAN-Detection的车辆检测  

Vehicle detection based on FVOIRGAN-Detection

作　　者：张浩[1,2,3,4] 杨坚华[1,2,3,4] 花海洋 ZHANG Hao;YANG Jianhua;HUA Haiyang(Key Laboratory of Opto-Electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院光电信息处理重点实验室,辽宁沈阳110016 [2]中国科学院沈阳自动化研究所,辽宁沈阳110016 [3]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169 [4]中国科学院大学,北京100049

出　　处：《光学精密工程》2022年第12期1478-1486,共9页Optics and Precision Engineering

基　　金：中科院创新基金项目(No.E01Z040101)。

摘　　要：为了解决点云处理过程中空间信息损失的问题,同时在融合过程中最大程度地提取可见光图像的纹理信息,本文提出了一种基于特征切片的激光点云与可见光图像融合车辆检测方法(FVOIRGAN-Detection)。在CrossGAN-Detection方法中加入了FVOI(Front View Based on Original Information)的点云处理思路,将点云投影到前视角度并把原始点云信息的各个维度切片为特征通道,在不降低网络性能的情况下显著提高点云信息利用效率。并且引入了相对概率的思想,采用鉴别器鉴别图像的相对真实概率替代绝对真实概率,使得融合图像提取的纹理信息更加接近真实的纹理信息。在KITTI数据集上进行检测性能实验验证结果表明,本文方法在容易、中等和困难三个类别中的AP指标分别达到97.67%、87.86%和79.03%。在光线受限的场景下,AP指标达到了88.49%,与CrossGAN-Detection方法相比提高了2.37%,提高了目标检测的性能。To solve the problem of spatial information loss in point cloud processing,and extract the texture information of visible images to the maximum extent during the fusion,a vehicle detection method based on laser point cloud and visible image fusion is proposed. The point cloud processing idea of front views based on the original information is incorporated into the CrossGAN-Detection method. The point cloud is projected to the front view angle,and each dimension of the original point cloud information is sliced into feature channels,significantly improving the utilization efficiency of the point cloud information without reducing network performance. The idea of relative probability is introduced,and the relative real probability,instead of the absolute real probability,of the discriminator is used to identify the image such that the texture information extracted is fused. The experimental results show that the AP indexes of this method in the three categories of easy,medium,and difficult of KITTI dataset are 97. 67%,87. 86%,and 79. 03% respectively. In a scene with limited light,the AP index reaches 88. 49%,which is 2. 37%higher than that of the CrossGAN-Detection method. Hence,target detection performance is improved.

关 键 词：点云处理 空间信息 相对概率 GAN 特征切片 车辆检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]