398results about How to "Reduce training difficulty" patented technology

The invention discloses a human body gesture identification method based on a depth convolution neural network, belongs to the technical filed of mode identification and information processing, relates to behavior identification tasks in the aspect of computer vision, and in particular relates to a human body gesture estimation system research and implementation scheme based on the depth convolution neural network. The neural network comprises independent output layers and independent loss functions, wherein the independent output layers and the independent loss functions are designed for positioning human body joints. ILPN consists of an input layer, seven hidden layers and two independent output layers. The hidden layers from the first to the sixth are convolution layers, and are used for feature extraction. The seventh hidden layer (fc7) is a full connection layer. The output layers consist of two independent parts of fc8-x and fc8-y. The fc8-x is used for predicting the x coordinate of a joint. The fc8-y is used for predicting the y coordinate of the joint. When model training is carried out, each output is provided with an independent softmax loss function to guide the learning of a model. The human body gesture identification method has the advantages of simple and fast training, small computation amount and high accuracy.

The invention discloses a quick image classification method based on deep learning, and the method comprises the following steps: 1, network building; 2, data set preprocessing; 3, network training; 4, image classification, wherein the image classification comprises the following substeps: 4.1, inputting a testing data set into the trained network model after preprocessing, and extracting the multi-scale features of a testing image; 4.2, inputting the extracted multi-scale features into a Softmax classifier, and outputting the probability that the testing image belongs to one class; 4.3, inputting probability that the testing image belongs to one class and a label corresponding to the image into an Accuracy network layer, and outputting the probability that the image is classified correctly. Through the above steps, the method can achieve the quick classification of the testing image. The method is short in training time, is convenient in training, and is high in classification precision.

The invention discloses a video smoke identification detection method based on transfer learning. The method comprises the steps of generating analog data to enlarge the number of smoke image samples;preprocessing the image data set; constructing a target detection network; using an ImageNet image dataset to pre-training an improved VGG-16 network; training a target detection network on the labeled smoke data set by adopting a transfer learning mode, wherein the feature extraction network part uses improved VGG-16 network pre-training weights to perform feature initialization; and extractinga key frame from the video, inputting the key frame into the model for identification and detection, and if smoke is found, returning coordinate information and positioning an area thereof in the video image. Model performance under the condition of limited smoke data is improved by using a transfer learning technology, and a smoke area in a video image can be automatically identified and positioned.

The invention discloses a structure adaptive CNN (Convolutional Neural Network)-based face recognition method, and belongs to the field of identity recognition. According to the method, the advantages of a conventional CNN directly extracting characteristics from a two-dimensional image for recognition are maintained, and in addition, a network structure is adaptively constructed to overcome the shortcoming of excessive dependence of the conventional CNN on human experiences. According to the structure adaptive CNN-based face recognition method, the network is extended according to network requirements, so that the controllability and adjustability of the network structure are achieved, ineffective training is also avoided, difficulty in training for face recognition is lowered, and in addition, an optimal face recognition network structure is obtained; by adaptive network extension advantages, a newly added face sample can be relearned on the basis of maintaining early recognition results, retraining overhead is reduced, and incremental learning is implemented; the automatic intelligent face recognition method is low in training difficulty and high in accuracy under a big data condition.

The invention discloses an image splicing tampering positioning method based on a full convolutional neural network. The method includes: establishing a splicing tampered image library; initializing an image splicing tampering positioning network based on a full convolutional neural network, and setting a training process of the network; initializing network parameters; reading a training image, performing training operation on the training image, and outputting a splicing positioning prediction result of the training image; calculating an error value between a training image splicing positioning prediction result and a real label, and adjusting network parameters until the error value meets a precision requirement; performing post-processing on the prediction result of which the precisionmeets the requirement by using a conditional random field, adjusting network parameters, and outputting a final prediction result of the training image; and reading the test image, predicting the test image by adopting the trained network, carrying out post-processing on a prediction result through a conditional random field, and outputting a final prediction result of the test image. The methodhas high splicing tampering positioning precision, the network training difficulty is low, and the network model is easy to converge.

The invention relates to a bridge crack image barrier detection and removal method based on a generative adversarial network. The method comprises the steps that first, multiple barrier pictures are collected, then tags are added, and the pictures with the tags are input into a Faster-RCNN for training; multiple barrier-containing crack pictures are collected, and barrier position calibration is performed through the Faster-RCNN; second, multiple barrier-free crack pictures are collected, and the pictures are turned over to amplify a dataset; third, the amplified dataset is input into the generative adversarial network to train a crack generation model; fourth, information erasure is performed on the positions of barriers in the barrier-containing crack pictures to obtain damaged images; and fifth, the damaged images are input into a cyclic discrimination restoration model for iteration, and then restored crack images are obtained. Through the method, barrier information in the crack pictures can be accurately detected and removed, the peak signal-to-noise ratio of the restored crack images is increased by 0.6-0.9dB compared with before, and therefore a large quantity of crack images with a high restoration degree are generated under a finite crack dataset condition.

The invention discloses a license plate detection model based on an improved YOLOv3 network and a construction method. The improved YOLOv3 network is used for inputting a license plate image and extracting three feature maps of different scales; carrying out up-sampling on the obtained three feature maps with different scales, scaling depth features to the same proportion, then carrying out down-sampling, and carrying out decoding through a constructed convolution layer to generate a feature map after feature enhancement; performing feature aggregation on the generated feature maps of the three different scales after feature enhancement and the feature maps of the three different scales extracted from the YOLOv3 feature extraction network to generate a feature pyramid, and obtaining an improved license plate detection model of the YOLOv3 network; and training the license plate detection model to obtain a final model. According to the method, the detection speed is greatly improved, thepyramid multi-scale feature network is introduced to enhance the features of the backbone network and generate a more effective multi-scale feature pyramid, and the features are better extracted fromthe input image.

The invention discloses a single convolutional neural network-based facial multi-feature point locating method. The method comprises the steps of expanding training samples; according to facial feature point coordinates corresponding to the samples and provided by a data set, determining a human face boundary frame; expanding data by adopting four operations of zooming, rotation, translation and overturning to made up for the deficiency of feature point tagging of training images; according to the human face boundary frame, extracting a human face image, and performing normalization processing; and finally designing a network structure, training a network, setting a learning rate of the network and a data processing quantity each time, and finishing facial multi-feature point locating. According to the method, the network structure is simplified; the training difficulty is lowered; the network structure can extract more global advanced features and express facial feature points more accurately; the locating effect on the facial feature points under variable complex conditions is good; and the facial multi-feature point locating can be realized.

The invention discloses a training method of an expression migration model applied to the field of artificial intelligence. The method comprises the steps of obtaining a source domain face image, a target domain face image and a face feature image; obtaining a synthetic face image through a to-be-trained expression migration model; obtaining a first discrimination result and a second discrimination result through the discrimination network model; obtaining a category feature vector through an image classification model; and updating model parameters of the to-be-trained expression migration model according to the category feature vector, the first discrimination result and the second discrimination result until a model convergence condition is met, thereby obtaining the expression migration model. The invention further discloses an expression migration method and device. According to the invention, complex image processing does not need to be carried out on the face image, the trainingdifficulty and the training cost are reduced, and the expression migration model can output a more real face image.

The invention discloses a method for generating a new sample based on a generative adversarial network and an adaptive proportion. The method comprises the following steps: S1, adding noise generatedby the generative adversarial network to the input sample directly and close to the distribution of the input sample; S2, constructing an adaptive proportion according to that variance of the sample,fusing the input sample and the noise generated by the generative adversarial network to generate a new sample, and adjusting the proportion of the noise and the input sample accord to the adaptive proportion of the new sample; S3 supplementing the original sample information for the new sample through the pixel addition operation, and generating a final sample which is beneficial to improving theDNN detection rate. The method improves the accuracy of DNN, and the cost is relatively small, and the complexity is lower.

The invention discloses a text generation image method and device. The text generation image method comprises the following steps: step 1, coding a natural language text describing the image to obtaina text semantic embedded representation; 2, mixing the text semantic embedding representation obtained in the step 1 with random noise, reading the text semantic embedding representation by a cyclicneural network transcoder, outputting the object hidden coding of each step by the random noise and the hidden state of the cyclic neural network transcoder; 3, decoding the hidden coding of each stepobject output from the step 2 to generate step images, and finally fusing all the step images to obtain the generated images; 4, carrying out confrontation training on that generate image and the real image. The generator of the invention decodes and generates the foreground and background pixel set of an image according to the object hidden coding through multi-step transcoding, and fuses the foreground and background pixel set of the image to generate a high-quality image, thereby reducing the training difficulty of directly generating the image.

An image processing method based on a neural network, an image processing apparatus, and a computer-readable storage medium are provided. The image processing method comprises the following steps of:obtaining an input image; classifying the input image by using neural network to obtain multiple category labels, wherein the neural network comprises a first sub-neural network and a second sub-neural network, The second sub-neural network comprises a plurality of branch networks, at least two branch networks of the plurality of branch networks are arranged in parallel, and the step of classifying the input image by using neural network to obtain multiple category labels comprises the following steps: extracting basic feature information of the input images by the first sub-neural network toobtain a plurality of first feature images; Dividing a plurality of first feature maps to obtain a plurality of first feature map groups corresponding to the plurality of branch networks one by one;respectively processing a plurality of first feature map groups by a plurality of branch networks to obtain a plurality of class tags.

The embodiment of the invention provides a key point detection method and device. The method comprises the steps: enabling a current frame image in video information to serve as the input of a human body detector, so as to calculate and output a human body detection frame vector which is used for cutting the current frame image, and an attitude probability value in the current frame image; Clipping the current frame image according to the human body detection frame to obtain a human body image block; And taking the attitude probability value and the human body image block as input of a featuredetector to calculate and output key points in the current frame of image. The method can effectively solve the problem that human body feature detection is difficult to execute in real time in the mobile terminal, reduces the network complexity in the key point detection process, and provides the detection precision.

The invention discloses a biological ink used for 3D biological printing and a preparation method thereof. The biological ink comprises the following components in percentage by mass: 8%-20% of a hydrophilic polymer with the crosslinking function, 10-20% of a water-soluble enzyme with the crosslinking function, 0.1%-1% of bacterial cellulose, and the balance a sterile PBS solution. The biologicalink can be used for 3D printing after mixed with cells, and the shear thinning properties of the used materials make the biological ink have the characteristics of self-supporting after extrusion. Theused materials have good cell compatibility, the enzyme crosslinking agent forms a hydrogel at physiological temperature (about 37 DEG C), the reaction conditions are mild, the cells are not damaged,a hydrogel support has relatively good stability in a culture medium, and thereby cell proliferation can be supported.

The invention provides a punctuation mark prediction method based on a self-attention mechanism, which comprises the following steps of performing voice recognition based on an automatic voice recognition technology to obtain a punctuation mark-free text; processing the punctuation-free text to obtain a text sequence; and constructing a punctuation mark prediction model, and importing the text sequence into the model to complete punctuation mark prediction of the text sequence. According to the punctuation prediction method based on the self-attention mechanism provided by the invention, the punctuation prediction of the speech recognition text is realized by constructing the punctuation prediction model, the problem of gradient disappearance is effectively alleviated, the feature transferis enhanced, and the long-term dependency relationship of the text is effectively established. Meanwhile, compared with a previous model, the additional parameters are not needed, the transmitted data size is effectively reduced, and the training difficulty of the parameters is reduced.