A method and system for generating a subway environment target training set
A technology for generating systems and training sets, applied in neural learning methods, biological neural network models, instruments, etc.
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Embodiment 1
[0076] figure 1 It is a flow chart of the method for generating the subway environment target training set of the present invention. Such as figure 1 As shown, a subway environment target training set generation method, including:
[0077] Step 101: Obtain and label the subway environment pictures with targets and various non-metro environment scene pictures.
[0078] Step 102: According to the subway environment picture, train to obtain the first generation network, specifically including:
[0079] Randomly extract a picture of the subway environment with the target and add random noise, substitute the picture of the subway environment with the target into the first generation network as a condition, and train to obtain the first generation network. The preliminary model of the first generation network uses PixelRNN model and weights.
[0080] Step 103: Generate a first random graph according to the first generation network.
[0081] Step 104: Train the first judgment ne...
Embodiment 2
[0111] figure 2 Generate a system structure diagram for the subway environment target training set of the present invention. Such as figure 2 As shown, a subway environment target training set generation system includes:
[0112] The first obtaining module 201 is used to obtain and mark the subway environment picture with the target and various non-metro environment scene pictures;
[0113] The first generation network determination module 202 is used to train the first generation network according to the subway environment picture;
[0114] The first random graph generation module 203 is configured to generate a first random graph according to the first generation network;
[0115] The first decision network determination module 204 is used to train the first decision network according to the subway environment picture and the first random graph, to obtain the trained first decision network;
[0116] A first update module 205, configured to update parameters of the firs...
Embodiment 3
[0139] Step 1: Prepare the target data of concern in various scenarios, including target data in office, field, day, night and other scenarios, and prepare conventional target data in the subway environment, including tracks, communication equipment, etc., and prepare for the non-metro environment routine target data, such as train tracks during the day, communication equipment in an office environment, etc.;
[0140] Step 2: The first stage of training the generative network G 0 , classify and label various environmental scene data (including subway data), and substitute various labels of the generated network and subway environment into the generator as condition c, and the generator first passes in a fully connected layer to generate a Gaussian distribution N[ mu 0 (φ c ), ∑ 0 (φ c )], and then upsample the Gaussian distribution to generate a random graph
[0141] Step 3: The first stage of training the decision network D 0 , for G 0 generated random graph is spa...
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