Attention mechanism based for vehicle detection from point cloud

Zixiang Kang; Shengzhe Wang; Yuyong Cui; Zhao Song; Pengxin Kang; Yinfu Long; Yunpeng Wang

doi:10.1117/12.2660035

3 February 2023 Attention mechanism based for vehicle detection from point cloud

Zixiang Kang, Shengzhe Wang, Yuyong Cui, Zhao Song, Pengxin Kang, Yinfu Long, Yunpeng Wang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111I (2023) https://doi.org/10.1117/12.2660035
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

3D object detection from lidar point cloud is a key technology in autonomous driving. As the sparseness, irregular and large amount of lidar point cloud data, which lead to slow operation efficiency of neural network and low detection accuracy. In order to address this problem, we research on vehicle object detection form lidar point cloud in this paper. In the data preprocessing stage, we use the point cloud reduction method based on density clustering (DBSCAN) to remove the sparse outliers points and noise points from the point cloud, and better retain the target features. The simplified point cloud makes the network converge faster in the training phase, effectively reduces the network computing overhead, and reduces the training time by ~40%. In order to make the network to get a better detection ability, we also add an attention network (Point Attention) to learn the key features from the target point cloud. The experimental results show that our proposed method successfully improves the network operation efficiency, and the accuracy of vehicle object detection is also increased to 89.5%.

Citation Download Citation

Zixiang Kang, Shengzhe Wang, Yuyong Cui, Zhao Song, Pengxin Kang, Yinfu Long, and Yunpeng Wang "Attention mechanism based for vehicle detection from point cloud", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111I (3 February 2023); https://doi.org/10.1117/12.2660035

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