The invention discloses a method for generating an object detection model, and the method comprises the steps: obtaining a training image containing annotation data, and enabling the annotation data to be the position and category of a target object in the training image; inputting the training image into a pre-trained object detection model for processing, the object detection model comprising afeature extraction module, a fusion module and a prediction module which are mutually coupled, the feature extraction module being suitable for performing convolution processing on the training imageto generate at least one initial feature map; the fusion module is suitable for performing up-sampling processing on the initial feature map to obtain a feature map after feature fusion; the prediction module is suitable for predicting the category and the position of the target object from the feature map; and on the basis of the annotation data and the predicted object category and position, training a pre-trained object detection model to obtain a trained object detection model as a generated object detection model.

A method of generating object detection model

The embodiment of the invention discloses an image processing method, an electronic device and a computer readable storage medium The method comprises the steps of obtaining to-be-processed target image data; based on a preset neural network model, taking N network output layers as prediction layers to perform multi-scale prediction on the target image data to obtain a first prediction result, the first prediction result comprising at least one prediction frame, and N being an integer greater than 1; and performing the step-by-step screening processing on the at least one prediction frame toobtain a target prediction result, so that the pneumonia diagnosis efficiency and accuracy based on the chest X-ray film can be improved

Image processing method, electronic device and computer readable storage medium

The invention discloses a deep learning-based multi-source heterogeneous data fusion method for a fully mechanized coal mining face, and is applicable to the technical field of intelligent mining of coal mines. The method comprises steps of establishing a deep learning fusion model of the multi-source heterogeneous data of the fully mechanized coal mining face according to the multi-source heterogeneous data information of the fully mechanized coal mining face in the mining process; establishing a multi-source heterogeneous data model of the fully mechanized coal mining face; unifying the spatial reference, the data attribute and the element relation of the multi-source heterogeneous data model; generating an irregular triangular network of the multi-source heterogeneous data model of thefully mechanized coal mining face; and extracting characteristic parameters of the irregular triangular network, verifying whether an iteration condition of a deep learning algorithm is met or not, and establishing a deep learning fusion model of the multi-source heterogeneous data of the fully mechanized coal mining face. According to the method, heterogeneous data of various modes of the fully mechanized coal mining face in the mining process can be quickly fused, and the data fusion efficiency and accuracy are effectively improved.

Deep learning-based multi-source heterogeneous data fusion method for fully mechanized coal mining face

A multi-task convolutional neural network customer behavior analysis method based on a dense network comprises the following steps: firstly, cleaning data, dividing the cleaned data into a training test set, and correspondingly compiling a dataloader; Then constructing a Dense _ Block module and a Transtion_ layer module, and constructing a multi-task convolutional neural network of the dense network based on the two modules; And carrying out data enhancement and standardization on the training picture, pre-training the model by using a large learning rate, and attenuating the fine tuning model by using a small learning rate and a small weight. And finally, effectively and quickly identifying the behavior of the customer by using the dense network multi-task convolutional neural network. Customer behavior analysis in a shopping mall store is completed by using a single neural network, so that the self capability of the store is improved; According to the invention, the convolutional neural network of the dense network is used to analyze a plurality of attributes of the customer at the same time, and the use efficiency is improved.

multi-task convolutional neural network customer behavior analysis method based on a dense network

The invention discloses a model training method and a domain self-adaptive visual position identification method and device, and belongs to the technical field of computer vision, and the method comprises the steps: building an image feature extraction model based on a deep neural network; constructing a training set according to the standard data set, wherein each training sample in the trainingset comprises a target image, a positive sample and s negative samples; using the training set to train the image feature extraction model; wherein in the image feature extraction model, the feature extraction network comprises a plurality of first networks which are cascaded; wherein the first network is formed by sequentially connecting one or more second networks and a maximum pooling layer, and the maximum pooling layer is used for feature selection; the second network comprises convolution layers which are connected in sequence and used for feature extraction; the batch standardization layer is used for carrying out zero-mean standardization processing; the activation function layer is used for carrying out activation processing; the local feature aggregation network is used for aggregating local features to obtain feature vectors of the image. According to the invention, the robustness of visual position identification can be improved.

Model training method, domain-adaptive visual position identification method and device

An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

Joint training of neural networks using multi-scale hard example mining

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Joint training of neural networks using multi-scale hard example mining