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13379 results about "Convolutional neural network" patented technology

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of these networks makes them prone to overfitting data. Typical ways of regularization include adding some form of magnitude measurement of weights to the loss function. However, CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble more complex patterns using smaller and simpler patterns. Therefore, on the scale of connectedness and complexity, CNNs are on the lower extreme.

Visual recognition and positioning method for robot intelligent capture application

The invention relates to a visual recognition and positioning method for robot intelligent capture application. According to the method, an RGB-D scene image is collected, a supervised and trained deep convolutional neural network is utilized to recognize the category of a target contained in a color image and a corresponding position region, the pose state of the target is analyzed in combinationwith a deep image, pose information needed by a controller is obtained through coordinate transformation, and visual recognition and positioning are completed. Through the method, the double functions of recognition and positioning can be achieved just through a single visual sensor, the existing target detection process is simplified, and application cost is saved. Meanwhile, a deep convolutional neural network is adopted to obtain image features through learning, the method has high robustness on multiple kinds of environment interference such as target random placement, image viewing anglechanging and illumination background interference, and recognition and positioning accuracy under complicated working conditions is improved. Besides, through the positioning method, exact pose information can be further obtained on the basis of determining object spatial position distribution, and strategy planning of intelligent capture is promoted.

Human face detection method and detection device based on multi-task cascade-connection convolution neural network

The invention discloses a human face detection method and a detection device based on multi-task cascade-connection convolution neural network, wherein the method comprises: establishing a cascade-connection multi-level convolution neural network; using the human face front samples, the human face back samples, some parts of the human face and the human face's key point samples as the training samples to train the multi-level convolution neural network to learn the tasks of human face categorizing, human face area position regression and human face's key point positioning; and utilizing the well trained multi-level convolution neural network to make human face detection from the to-be-detected image wherein in the training stage, both the online manner and the offline manner are combined to extract the human face back samples as the training samples. According to the invention, based on the cascade-connection multi-level convolution neural network, it is possible to learn the characteristics with stronger robustness, and at the same time, through the combination of the online manner and the offline manner to extract the back samples, the categorizing capability of the network is enhanced, so that the detection capability and the accuracy of the network are increased, and that the running speed of the method in an actual product is ensured.

Ship object recognition method based on multilayer convolution neural network

The invention discloses a ship object recognition method based on a multilayer convolution neural network, and the method comprises the steps: S1, employing the existing images, parameters and model data for the building of a ship sample library, and continuously increasing the data in a use process through target detection; S2, carrying out the ship target feature training under the frame of a convolution neural network, and forming visible light/infrared and two-dimensional/three-dimensional fusion ship feature knowledge library through the recognition training of the ship sample library, wherein the ship feature knowledge library is used for the classification and recognition of ships; S3, carrying out the data collection of a ship target: carrying out the real-time high-resolution collection of the visible light or infrared video data of the ship target on the sea; S4, carrying out the detection of the ship target on the sea; S5, carrying out the coarse classification of the imagesof the ship target; S6, carrying out the fine classification and recognition of the ship target based on a depth neural network completed through the ship target feature training, and accurately recognizing the type of the recognized ship. The method overcomes a difficulty in recognition of the ship target.
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