37 results about "Deep cnn" patented technology

The invention discloses a target detection method based on Faster-RCNN reinforcement learning and relates to the field of image processing. The method comprises acquiring an image to be tested; introducing the image to be tested into a Faster-RCNN; modifying the network structure of the CNN in the Faster-RCNN to replace a convolution module in the network structure of the last scale with a hourglass module; extracting the feature of the image to be tested by the CNN to generate feature mapping maps; importing the last feature mapping map into an RPN; vectorizing a feature mapping map corresponding to a candidate region selected by the RPN and then classifying the feature mapping map by a classifier to obtain a detection result. The method modifies the network structure of the CNN, replacesthe ordinary convolution module in the deep network with the hourglass module, performs reinforcement learning on the semantic information carried by the deep features extracted by the deep CNN, hierarchically highlights the semantic information of an object, and reduces the false negatives and false positives to a certain extent.

Described is a system for adapting a deep convolutional neural network (CNN). A deep CNN is first trained on an annotated source image domain. The deep CNN is adapted to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space, and using the domain agnostic features to map the joint latent space to annotations for the target image domain.

The invention provides a human body identification method and a human body identification device. The method comprises the steps of acquiring image information; carrying out deep processing on the image information to obtain a depth image and a color image; carrying out human body detection of a deep CNN (Convolutional Neural Network) on the color image and determining a human body boundary frame in the color image; judging whether a unique human body exists in a human body boundary frame area, corresponding to the human body boundary frame, in the depth image; if two or more human bodies exist in the human body boundary frame area, separating the two or more human bodies; and determining the number of the human bodies in the image information according to the number of the human bodies in the human body boundary frame area in the depth image. According to the human body identification method and the human body identification device, secondary identification for the human bodies identified in the color image is realized based on the depth image, the overlapped human bodies are prevented from being misjudged as one human body, the human body identification is assisted by use of the image information of the depth image, and thus the accuracy degree of the human body identification is improved.

The invention belongs to the field of intelligent transportation and relates to a method for detecting a safety belt based on the adoption of deep CNN of image at traffic monitoring points. The method establishes a sample database and conducts deep CNN training on the sample database, and adopts training parameters to determine whether a driver in a newly input image wears a safety belt. The method comprises the following steps of extracting a gray scale image of a driver area, establishing a sample database, designing a deep CNN network, training the deep CNN network and determining the result of individual image. The method extracts a window area by using upper and lower edge information of a front window of a vehicle, conducts auxiliary positioning of the driver area by using a plate area, does not need to use characteristics of manual design classification, and has high precision in determining whether the driver wears the safety belt. The method has simple principle, is easy to operate, has small errors, is safe and reliable, has strong application values, and is environmental friendly.

The invention relates to a Wi-Move behavior perception method based on a CNN. The Wi-Move behavior perception method comprises the following steps: (1) preprocessing Wi-Move data; (2) performing humanbody behavior perception based on CNN; (3) constructing a Wi-Move input feature map; (4) designing a Wi-Move network; and (5) optimizing the Wi-Move network model. Aiming at the problem that a feature extraction and classification sensing method is not comprehensive in feature extraction and is only suitable for sensing fewer behavior types, the invention provides a CNN-based WiMove behavior sensing method, and compared with the feature extraction and classification sensing method, the CNN-based WiMove behavior sensing method has higher recognition accuracy in occasions of sensing multiple types of behaviors. Amplitude and phase information of all CSI subcarriers is hierarchically extracted by a deep CNN network, and feature information is more comprehensive.

The invention discloses a vaginal secretion wet sheet candida detection method based on a Hough round detection and deep CNN (convolutional neural network). The method comprises the following steps of (1) by aiming at the round features (oval or 8 shape) of the candida, dividing a candidate region of the vaginal secretion wet sheet candida on the basis of a Hough round detection and morphological operation method; (2) performing feature extraction based on the deep CNN; (3) building a CNN-SVM (support vector machine)-based classification model, and realizing the candida identification. The image processing and recognition method based on the Hough round detection and deep CNN is used; the vaginal secretion wet sheet candida detection can be conveniently, economically and efficiently realized; the requirements of intelligence, high speed and high precision of the medical application can be met.

A computer-implemented method of localization for an indoor environment is provided, including receiving, in real-time, a dynamic query from a first source, and static inputs from a second source; extracting features of the static inputs by applying a metric learning convolutional neural network (CNN), and aggregating the extracted features of the static inputs to generate a feature transformation; and iteratively extracting features of the dynamic query on a deep CNN as an embedding network and fusing the feature transformation into the deep CNN, and applying a triplet loss function to optimize the embedding network and provide a localization result.

The invention discloses a remote sensing image dense target deep learning detection method. The method is used for extracting a remote sensing image dense target. According to the method, an image isinputted into a deep CNN basic network, so that a feature map can be obtained; deep convolution features are inputted into a dense target extraction framework, so as to be subjected to region of interest (RPN branch) extraction, object classification and rectangular frame regression; for RPN branches, a high density bias sampler is adopted to excavate more samples (difficult samples) with high density to improve detection performance; Soft-NMS is employed to reserve more positive objects after the dense target extraction framework; and finally, a refined rectangular frame is outputted, so thatthe quantity of dense objects can be counted.

The invention discloses a remote sensing scene classification method based on a branch feature fusion convolutional network, and relates to a remote sensing scene classification method. The objectiveof the invention is to solve the problem that an existing remote sensing scene image has a complex space structure. According to the method, the problems that most existing deep CNN models with good classification performance have high complexity, shallow CNN models have low complexity, but the classification accuracy cannot meet the requirements of practical application in the remote sensing field are solved. The method comprises the following steps: 1, establishing an LCNN-BFF network model; 2, training a network model by adopting the data set; 3, verifying the accuracy of the pre-trained model by adopting the test set, obtaining the trained model if the accuracy meets the requirement, otherwise, continuing to train the model until the accuracy meets the requirement; and 4, adopting thetrained model to classify the remote sensing scenes to be identified. The method is applied to the field of remote sensing scene classification.

The invention belongs to the technical field of ground-based meteorological cloud picture classification, and particularly relates to a ground-based meteorological cloud picture classification methodbased on cross validation deep CNN feature integration. According to the method, firstly, a convolutional neural network model is utilized to extract deep CNN features of a foundation meteorological cloud image, then multiple times of resampling of the CNN features is performed based on cross validation, and finally, identification of the cloud shape of the foundation cloud image is performed based on a voting strategy of multiple times of cross validation resampling results. According to the method, the ground-based meteorological cloud images are automatically classified, and an adaptive end-to-end automatic cloud recognition algorithm directly based on the original cloud images without any image preprocessing is realized. The proposed algorithm relates to the fields of computer vision,machine learning, image recognition and the like. The proposed algorithm fully overcomes the non-robustness of a single CNN feature cloud classification result and the high calculation overhead of multi-time deep convolutional neural network integration, and at the same time, ensures that the proposed algorithm has high classification accuracy and noise stability.

The invention provides a tracking method based on dual-model adaptive kernel correlation filtering, which comprises the following steps: initializing the position of a pre-estimated target, calculating a Gaussian tag, and establishing a main feature model and an auxiliary feature model; extracting HOG features to serve as features of a main feature model, extracting deep convolution features to serve as features of an auxiliary feature model, and setting initialization parameters; calculating a response layer of the pre-estimated target by utilizing the main characteristic model, and obtainingan optimal position and an optimal scale of the pre-estimated target by the response layer through a Newton iteration method; if the maximum confidence response value max of the response layer corresponding to the optimal scale is greater than an empirical threshold u, determining a pre-estimated target position, and updating the main feature model; if max is smaller than or equal to an empiricalthreshold u, stopping updating the main feature model, expanding a search area, extracting CNN features of a target pre-selected area, performing dimensionality reduction on deep CNN features by using a PCA technology, estimating a new target position by using the dimensionality-reduced CNN features, and updating an auxiliary feature model until thevideo sequence ends.

The invention provides a user trajectory-based interest point recommendation method which comprises the following steps of: firstly, collecting user trajectory data, deleting ping-pong data and driftdata in an original data set to reduce the influence of noise data on the accuracy of the recommendation method, and then extracting attribute characteristics in different regions; performing statistics on surrounding pedestrian flow data and uplink and downlink pedestrian flow data of a position where a POI is located, constructing a training sample set, designing a model WideDeep CNN by combining a Wide model and a Deep CNN model, and predicting a score value of the POI to be recommended in a region to be monitored by using the model WideDeep CNN. According to the method, the training sampleset is constructed by fully using user trajectory data; scoring fingers of different types of POIs are generated based on a neural network model, and then a POI list is generated according to the scoring values. According to the method, time-space information contained in the mobile big data is deeply mined, the requirements of the public are analyzed, and the interest point recommendation problem is better solved.

The invention discloses an environmental sound classification analysis method and device and a medium. The method comprises the steps of performing data enhancement on voice training data; carrying out data preprocessing and carrying out feature extraction on the environment sound to obtain a feature vector; and carrying out model training on the feature vector by adopting a deep CNN network to obtain a multi-classification model of the environmental sound and outputting the multi-classification model. The data enhancement method is adopted, experiments show that the training effect is greatly improved compared with that without data enhancement, and the practical value of the system is improved.