39results about How to "Reduce the amount of model parameters" patented technology

The invention discloses an image defogging method based on a multi-scale dark channel prior cascade deep neural network. The method comprises the following steps: 1, establishing an atomized image training set; 2, defogging a single random foggy image; 3, calculating a loss objective function of the original single foggy image; 4, updating the weight parameter set; 5, calling a new single random foggy image, circulating the step 2 to the step 4 until the loss target function of the original single foggy image is smaller than the loss target function threshold, and determining a final cascade defogging model; and 6, defogging a single actual foggy image. According to the invention, the convolutional neural network is used to estimate dark channel and global illumination parameters on imagesof different scales. The deep neural network is used as a model, and then the dark channel and the defogged image are fused step by step. Finally, the defogged image is obtained through supervised learning. The feature modeling capability of the deep neural network is effectively utilized. The parameter fusion of different scales is achieved. The high-resolution defogged image can be obtained under the condition of few model parameters.

The present invention provides a real-time detection method for pedestrians and vehicles based on a lightweight deep network. The lightweight deep network uses 5 modules + 3 kinds of convolution operations, and the meta-module only contains 2 kinds of convolution operations to realize the feature extraction function. . The jump connection mode between different modules and the more robust feature spectrum fusion technology of the present invention enable the network to achieve a better detection effect on pedestrians and vehicles when the model parameters are small, and can effectively detect images or Pedestrian vehicles in the video. The new deep network proposed by the present invention has the advantages of small model parameters, small computational complexity and high detection accuracy, and can realize real-time detection of pedestrians and vehicles on the embedded platform, and has good practicability and real-time performance .

The present disclosure provides a model training method, a demand determination method, and an apparatus, wherein the model training method includes: for each first historical time period in a plurality of first historical time periods, obtaining data obtained in the first historical time period The number of orders for each grid area pair in the internal model; input the order quantity of each grid area pair in the first historical period of the previous preset number into the original model to obtain the first reference order quantity; The order quantity of each grid area pair in a historical time period is input into the compression model, and the second reference order quantity is obtained; based on the first reference order quantity, the second reference order quantity and each grid area in the last first historical time period The compressed model is trained on the number of true orders. The embodiment of the present disclosure compresses the original model through knowledge distillation, and the obtained compressed model uses fewer parameters, which can reduce the consumption of computing resources in the process of determining the number of orders.

The invention discloses an expanded fully convolutional neural network and a construction method thereof. The neural network includes a sequentially connected convolutional neural network, a feature extraction module, and a feature fusion module. The construction method is as follows: Select the convolutional neural network: remove the fully connected layer and classification layer used for classification in the convolutional neural network, leaving only the middle convolutional layer and pooling layer, and remove the convolutional layer and pooling layer Extract the feature map; construct the feature extraction module: the feature extraction module includes a plurality of series-connected expansion upsampling modules, and each expansion upsampling module includes a feature map merging layer, an expansion convolution layer and a deconvolution layer; construct Feature fusion module: The feature fusion module consists of a dense dilated fusion convolution block and a deconvolution layer. The invention effectively solves the problem of feature extraction and fusion in the convolutional neural network, and can be applied to the pixel-level labeling task of images.