The invention discloses a three-dimensional image segmentation method and system based on a full convolutional neural network, and the method comprises the following steps: step1, collecting and obtaining a sequence image, and carrying out the marking of the sequence image, and obtaining training sample data; step 2, performing normalization preprocessing on the training sample data obtained in the step 1; step 3, applying the sample data processed in the step 2 to carry out supervised training on pre-constructed 3-D full convolution residual U-net network model, training to a preset convergence condition, and obtaining a trained three-dimensional image segmentation model; and step 4, after normalization processing is carried out on the sequence image data to be segmented, inputting the sequence image data into the three-dimensional image segmentation model trained in the step 3, and obtaining a sequence image segmentation result. The continuity information of the sequence can be fullyutilized, and a relatively good result can be obtained in three-dimensional image segmentation.

A three-dimensional image segmentation method and system based on a full convolutional neural network

The invention is applicable to the technical field of target image segmentation, and provides a target image segmentation method, a device and a terminal device. A lightweight convolution neural network is used for segmenting the first frame of the video image to obtain a target image; using a preset target tracking method to track the target image of a subsequent frame of the video image to obtain a tracking area of the target image; the lightweight convolution neural network is used for segmenting the tracking region of the target image to obtain the target image. The method can improve thespeed of target image segmentation.

Target image segmentation method, device and terminal device

The invention discloses a CNN (Convolutional Neural Network)-based voiceprint recognition method for anti-record attack detection. The CNN-based voiceprint recognition method comprises the following steps: step S101, acquiring to-be-detected voice frequency and establishing a voiceprint recognition data set; step S102, carrying out character extraction on the voice frequency of the voiceprint recognition data set, wherein extracted characters comprise a character MFCC (Mel Frequency Cepstrum Coefficient) and a bottleneck layer character; step S103, establishing a CNN by combining MobileNet andUnet; step S104, inputting the voiceprint recognition data set to the CNN for training; step S105, inputting the bottleneck layer character to the trained CNN by using testing voice frequency, thus obtaining a testing score for judging real talk or record voice frequency. The CNN-based voiceprint recognition method disclosed by the invention combines the characteristics of two models of the Unetand the MobileNet, has lower model complexity, i.e., lower model size, smaller computation resource loss and higher recognition accuracy rate, and can be transplanted and applied to a mobile phone side and an embedded device.

CNN (Convolutional Neural Network)-based voiceprint recognition method for anti-record attack detection

The invention discloses a vehicle control method and device, and a vehicle, and relates to the technical field of automatic driving and deep learning. The specific implementation scheme comprises thefollowing steps of acquiring a current image in a front portion of a vehicle, and acquiring a parking track path of the vehicle; obtaining a deep convolutional neural network model corresponding to the parking track path; inputting the current image into the deep convolutional neural network model to obtain slope information of a road section in front of the vehicle; determining a longitudinal acceleration of the vehicle according to the slope information; and controlling the vehicle according to the longitudinal acceleration. According to the scheme, slope obtaining cost of the vehicle is reduced.

Vehicle control method and device, and vehicle

A method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV (701); and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV (703). The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint (705); extracting a second set of feature descriptors from the pre-built point cloud map (707); and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

Deep learning-based feature extraction for LIDAR localization of autonomous driving vehicles

The embodiment of the invention discloses a method and device for road image segmentation based on point cloud data and electronic equipment. The method comprises the steps of obtaining point cloud data of a road image, and performing dimensionality reduction to obtain corresponding three-dimensional tensor data; and segmenting the road image according to the three-dimensional tensor data by usinga lightweight deep neural network, wherein the lightweight deep neural network is obtained by training based on the three-dimensional tensor data. According to the invention, dimensionality reductionprocessing is performed on the collected point cloud data by adopting a spherical coordinate system, so that a sparse problem of the original collected point cloud data can be solved, and the speed of performing operation by using the deep neural network in a later period can be improved. Furthermore, the lightweight unet deep neural network framework based on a mobilenet network structure is utilized, thereby being capable of operating based on embedded equipment such as an FPGA, being capable of effectively improving the operation speed, and reducing the operation power consumption of the equipment.

Method and device for road image segmentation based on point cloud data and electronic equipment

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Method and device for road image segmentation based on point cloud data and electronic equipment