计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900079-6.doi: 10.11896/jsjkx.210900079
车爱博1, 张辉2, 李晨1, 王耀南2
CHE Ai-bo1, ZHANG Hui2, LI Chen1, WANG Yao-nan2
摘要: 文中在CIA-SSD单阶段三维目标检测模型的基础上,将模型中空间语义特征融合方式进行改进,通过一种基于注意力机制的多通道融合模块对两特征进行融合,提出了单阶段检测方法TFAF-SSD(Two-Feature Attentional Fusion Single-Stage object Detector),该方法主要由流形稀疏卷积网络提取点云的稀疏特征后,再由空间语义卷积层分别提取检测对象的空间语义特征,对融合后的输出特征进行预测,最后通过检测头输出最终的检测框。同时,文中还运用了不同于以往方法的数据增强方法,增强了模型的泛化性能,达到了提升检测精度的效果。在KITTI 3D公开数据集上进行了验证,在测试集中汽车检测方面得到了中等检测难度AP值为83.77%的检测结果,相比CIA-SSD模型的80.28%,所提方法提升了3.49%。
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