湖南大学学报（自然科学版）2025，Vol.52Issue(10)：108-119,12.DOI:10.16339/j.cnki.hdxbzkb.2025211
面向深度模糊的可部署单目3D目标检测方法
Deployable Monocular 3D Object Detection Method for Overcoming Depth Ambiguity
摘要
Abstract
Monocular 3D object detection encounters significant challenges due to depth ambiguity.Conventional 2D attention mechanisms have proven insufficient in mitigating this issue,and their high computational overhead further complicates deployment on vehicle-mounted mobile devices.To address these issues,this paper proposes a monocular 3D object detection algorithm based on 3D attention mechanism and multi-object bounding box module.Considering the depth ambiguity in 2D to 3D mapping,the 3D attention mechanism was first incorporated into the network design,which includes a depth information enhancement kernel and a position enhancement kernel with low computational complexity.Then,multi-object bounding box strategies employ pseudo-labels to alleviate the strict constraints of the original hard labels from depth labels perturbations.Hence,the precision of deep estimation is enhanced,improving the model's 3D spatial perception and generalizability for 3D object detection tasks.Experiments on the nuScenes dataset demonstrate that this algorithm outperforms existing monocular 3D object detection methods.Finally,with the aid of TensorRT tools,the model is successfully deployed on mobile devices in the automotive environment.On the Jetson AGX Xavier and Jetson Orin NX(16 GB)embedded platforms,the inference time per frame is 67 ms and 89 ms,respectively,enabling real-time detection of 3D objects.关键词
注意力机制/模型部署/深度模糊性/目标检测Key words
attention mechanism/model deployment/deep ambiguity/object detection分类
计算机与自动化引用本文复制引用
邓召学,郝丙森,龚胜,刘万里,王景炎..面向深度模糊的可部署单目3D目标检测方法[J].湖南大学学报（自然科学版）,2025,52(10):108-119,12.基金项目
国家自然科学基金资助项目(52072054),National Natural Science Foundation of China(52072054) （52072054）
重庆交通大学研究生科研创新项目(YYK202405),Research and Innovation Program for Graduate Students in Chongqing Jiaotong University(YYK202405) （YYK202405）
