No 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.