The invention relates to a pest and disease image generation method based on a generative adversarial network In the prior art, sampling images of pest and disease images are less By using the method of the invention, the above defect is overcome The method comprises the following steps of collecting and preprocessing trained images; based on a deep-convolution neural network model, constructing a discrimination network and a generation network; training the discrimination network and the generation network; and according to the trained generation network, generating the pest and disease images In the invention, according to a few of existing pest and disease images, a lot of pest and disease images which are similar to a reality are generated, a sample image is provided for pest and disease image identification, and problems that the pest and disease images in an actual field are less and acquisition cost is high are solved

Pest and disease image generation method based on generative adversarial network

A depth significance-based remote sensing image rapid retrieval method is disclosed and belongs to the field of computer vision. The method disclosed in the invention specifically relates to technologies such as in-depth learning, significance object detection, image retrieval and the like. According the method, remote sensing images are research objects, and in-depth learning technologies are used for researching a remote sensing image rapid retrieval method. A full convolution neural network is adopted for constructing a multitask significance object detection model which is used for doing significance detection tasks and semantic segmentation tasks at the same time, and depth significance characteristics of the remote sensing images are learnt in network pre-training processes. A depth network structure is improved, a Hash layer fine tuning network is added, and binary system Hash codes of the remote sensing images can be obtained via learning. Significance characteristics and the Hash codes are used comprehensively for similarity measurement. The method disclosed in the invention is of high application value for realizing accurate, highly efficient and feasible retrieval of the remote sensing images.

Depth significance-based remote sensing image rapid retrieval method

A monocular image depth estimation method based on multi-scale CNN and continuous CRF In the method, a CRF model is used to calculate single-point potential energy according to an output depth map of DCNN, paired sparse potential energy is calculated according to an input RGB image, and finally an optimized depth map is deduced using a maximized posterior probability (MAP) algorithm According to the method, optimized thoughts of multi-scale CNN and continuous CRF are combined, so that not only a depth map can be estimated with a relatively high precision, the contour of the obtained depth map can also be clear The depth estimated by means of the method has a relatively high resolution, and the obtained depth map can reserve depth detail information about all objects in a scenario, thereby having a better visual effect

Monocular image depth estimation method based on multi-scale CNN and continuous CRF

The invention discloses an identification method of emotional tendency of network comment texts and a convolutional neutral network model. The method comprises the steps as follows: grabbed network comment texts constitute a data set; word segmentation and text preprocessing are performed; all words subjected to text preprocessing are trained, and word vector representation of all words is obtained; the convolutional neutral network model is constructed and trained on a training set selected from the data set, and network parameters are updated with a back-propagating algorithm; in each step of training, noise is added to word vectors of an input layer for construction of adversarial samples, adversarial training is performed, and network parameters are updated with a random gradient descent algorithm; a classification model is obtained through repeated iteration to identify the emotional tendency of the network review texts. The convolutional neutral network model is used in the method and comprises the input layer, a convolution layer, a pooling layer and a classification layer. The adversarial samples can be classified correctly and the identification accuracy is improved.

Identification method of emotional tendency of network comment texts and convolutional neutral network model

The invention discloses a video enhancement and transmission method based on deep learning. Downsampling is carried out on a high-definition video at a video source end, thereby obtaining a low-definition video; the low-definition video is compressed in an existing video coding mode; and the compressed low-definition video is transmitted. The size of the video data is greatly reduced through downsampling and video coding, so the video traffic needing to be transmitted is correspondingly reduced, and an effect of reducing the bandwidth cost is achieved. At a user receiving end, a user receives the low-definition video, reconstructs the low-definition video by employing a super-resolution image reconstruction method of the deep learning and restores the low-definition video into a high-resolution video for the user to watch, so the video transmission bandwidth cost is effectively reduced. According to the method, the video transmission bandwidth cost is reduced by at least 50%; the resolution of the live broadcast video is improved; and the watching experience of a user is improved.

Video enhancement and transmission method based on deep learning

Disclosed is an image saliency detection method for generating a convolutional neural network model by means of adversarial training, falling within the field of computer vision and image processing The method comprises data pre-processing, network construction, selecting a suitable parameter, and using a stochastic gradient descent method and an impulse unit to perform training The data pre-processing is pre-processing the collected mass data and tags The network construction is designing a network structure and a specific kernel function Selecting a suitable parameter comprises a learning rate, a momentum factor and the number of images crammed into a network each time Using a stochastic gradient descent method and an impulse unit to perform training reduces the possibility of network overfitting By means of the method, a saliency map can be obtained more accurately

Dai Qionghai

Papers

Image saliency detection method based on adversarial network