CONVOLUTIONAL NEURAL NETWORK BASED ON CHANNEL-SPECIFIC CONVOLUTION FOR HIGH-SPEED OBJECT RECOGNITION OF 3D LiDAR

Provided is a convolutional neural network structure based on channel-specific convolution for high-speed object recognition of a 3D LiDAR, including: an inside-channel convolutional network for extracting features in channels of a LiDAR data; an outside-channel convolutional network for extracting features between the channels by using outputs for the channels of the inside-channel convolutional network and generating a feature map representing the extracted features between the channels; and a detection network searching location and class of an object by using the feature map generated from the outside-channel convolutional network. According to the invention, since a raw data coming from a LiDAR is directly used in a high-speed object recognition procedure of a 3D LiDAR, there is an effect that data loss does not occur.