基于融合采样和深尺约束的单目3D目标检测  

Monocular 3D Object Detection Based on Fused Sampling and Depth-scale Constraints

作　　者：孙虎成 臧可 SUN Hu-Cheng;ZANG Ke(College of Automation,Qingdao University,Qingdao 266071,China)

机构地区：[1]青岛大学自动化学院,青岛266071

出　　处：《计算机系统应用》2025年第4期34-44,共11页Computer Systems & Applications

基　　金：山东省自然科学基金(ZR2023QF089)。

摘　　要：针对单目图像中不同深度目标的尺度差异所导致的单目3D目标检测算法精度不佳的问题,提出一种基于融合采样和深尺约束的检测算法.首先,为增强采样特征对不同尺度目标的表征能力,构建多尺度特征融合模块(multi-scale fusion module,MFM),通过分层聚合和迭代聚合对不同层级、不同尺度的特征进行融合采样,从而提高对目标隐式尺度特征的提取能力.此外,构造深度尺度相关化模块(depth-scale correlation module,DSCM),利用深度与尺度之间的线性投影约束将不同尺度的目标补偿式放缩至同一特征水平,以此平衡模型对不同距离目标的关注度.基于KITTI数据集和Waymo数据集的定量结果表明,所提出的算法相较于同类算法在多种难度下的整体平均精度AP3D分别提升了1.56个百分点和3.07个百分点,验证了算法的有效性及泛化性,同时基于两类数据集的定性结果验证了该算法显著缓解了目标尺度差异对检测性能造成的影响.Aiming at the poor accuracy of monocular 3D object detection algorithms caused by the scale differences of objects with different depths in monocular images,a detection algorithm based on fused sampling and depth-scale constraints is proposed.Firstly,to enhance the ability of the sampled features to represent objects at different scales,a multi-scale fusion module(MFM)is constructed.It fuses the sampled features at different levels and scales through hierarchical aggregation and iterative aggregation,thereby improving the ability to extract implicit scale features of the objects.In addition,a depth-scale correlation module(DSCM)is constructed.It uses the linear projection constraint between depth and scale for compensatory scaling of objects at different scales to the same feature level,balancing the model's focus on objects at different distances.Quantitative results based on the KITTI dataset and Waymo dataset show that for both types of datasets,the proposed algorithm improves the overall average accuracy AP3D by 1.56 percentage points and 3.07 percentage points,respectively,compared to similar algorithms under multiple difficulties,which verifies the effectiveness and generalization of the algorithm.Meanwhile,qualitative results based on the two datasets validate that the algorithm significantly mitigates the impact of the object scale differences on detection performance.

关 键 词：3D目标检测 融合采样 多尺度特征 可变形卷积 关注度平衡 尺度放缩 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]