Abstract
Currently, there are various methods of LiDAR-based object detection networks. In this paper, we propose a channel-based object detection network using LiDAR channel information. The proposed method is a 2D convolution network with data alignment processing stages including a single-step detection stage. The network consists of a channel internal convolution network, channel external convolution network and detection network. First, the convolutional network within the channel divides the LiDAR data for each channel to find features within the channel. Second, the convolutional network outside the channel combines the LiDAR data divided for each channel to find features between the channels. Finally, the detection network finds objects with the features obtained. We evaluate our proposed network using our 16-channel lidar and popular KITTI dataset. We can confirm that the proposed method detects objects quickly while maintaining performance when compared with the existing network.
Highlights
Object detection involves the analysis of input data received through a sensor to identify the locations and classes of an object
This paper paper makes makes the thefollowing followingcontributions: contributions: First, First, we we propose propose an an object object detection detection network network based on a data adversely affect convolution due to the large amount of empty based on a Light Detection and Ranging (LiDAR) channel
We proposed a new method called object detection network based on a LiDAR
Summary
Object detection involves the analysis of input data received through a sensor to identify the locations and classes (pedestrians, dogs, cats, cars, etc.) of an object. At least 16 channels of LiDAR data are used for object detection. The first method is to detect objects by discretizing the LiDAR data into specific areas to create voxels. This first method uses voxels, which are. 2020, 9, 1122 are disadvantages of increasing the image size depending on the navigation range, and there are many blank spaces on the transformed image, which makes it difficult to recognize the object because of its low data density. LiDAR data adversely affect convolution due to the large amount of space when searching a wide range from a limited number of points based on distance. Using the channel-based method, the actual LiDAR data is 3D, but sinceused it can be.
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