TY - JOUR
T1 - CNN-Based Classification for Point Cloud Object with Bearing Angle Image
AU - Lin, Chien Chou
AU - Kuo, Chih Hung
AU - Chiang, Hsin Te
N1 - Funding Information:
This work was supported in part by the Intelligent Recognition Industry Service Center (IRIS) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan; and in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 106-2221-E-224-054 and Grant MOST 107-2221-E-224-050
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Convolutional neural network (CNN), one of the branches of deep neural networks, has been widely used in image recognition, natural language processing, and other related fields with great success recently. This paper proposes a novel framework with CNN to classify objects in a point cloud captured by LiDAR on urban streets. The proposed BA-CNN algorithm is composed of five steps: (i) removing ground points, (ii) clustering objects, (iii) transforming to bearing angle images, (iv) ROI selection, and (V) identifying objects by CNN. In the first step, ground points are removed by the multi-threshold-based ground detection to reduce the processing time. Then, a flood-fill-based clustering method is used for object segmentation. Those individual point cloud objects are converted to bearing angle (BA) images. Then, a well-trained CNN is used to classify objects with BA images. The main contribution of this paper is proposing an efficient recognition method that uses the information from point clouds only. In contrast, because most 3D object classifiers use the fusion of point clouds and color images, their models are very complicated and take a colossal amount of memory to store the parameters. Since the ground point detection and object clustering process all points along with the scanline-major order and layer-major order, the proposed algorithm performs better in terms of time consumption and memory consumption. In the experiment, three scenes from KITTI dataset are used for training and testing the proposed BA-CNN classifier, and the proposed BA-CNN achieves high classification accuracy.
AB - Convolutional neural network (CNN), one of the branches of deep neural networks, has been widely used in image recognition, natural language processing, and other related fields with great success recently. This paper proposes a novel framework with CNN to classify objects in a point cloud captured by LiDAR on urban streets. The proposed BA-CNN algorithm is composed of five steps: (i) removing ground points, (ii) clustering objects, (iii) transforming to bearing angle images, (iv) ROI selection, and (V) identifying objects by CNN. In the first step, ground points are removed by the multi-threshold-based ground detection to reduce the processing time. Then, a flood-fill-based clustering method is used for object segmentation. Those individual point cloud objects are converted to bearing angle (BA) images. Then, a well-trained CNN is used to classify objects with BA images. The main contribution of this paper is proposing an efficient recognition method that uses the information from point clouds only. In contrast, because most 3D object classifiers use the fusion of point clouds and color images, their models are very complicated and take a colossal amount of memory to store the parameters. Since the ground point detection and object clustering process all points along with the scanline-major order and layer-major order, the proposed algorithm performs better in terms of time consumption and memory consumption. In the experiment, three scenes from KITTI dataset are used for training and testing the proposed BA-CNN classifier, and the proposed BA-CNN achieves high classification accuracy.
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U2 - 10.1109/JSEN.2021.3130268
DO - 10.1109/JSEN.2021.3130268
M3 - Article
AN - SCOPUS:85120579544
SN - 1530-437X
VL - 22
SP - 1003
EP - 1011
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 1
ER -