The invention discloses a
visibility regression prediction method based on multi-
modal transfer learning and time coding, and relates to the technical field of
artificial intelligence. The method comprises the following steps: S1, dividing a
data set in different periods according to illumination characteristics, and splitting each period into a
training set, a
verification set and a
test set; s2, preprocessing the
data set; s3, constructing an initial model containing a pre-training
deep learning network, a time coding module and a multi-layer
perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the
test set to determine a final model. According to the method, complementarity of image and
time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a
multilayer perceptron combination technology enhances expression, and
overfitting is effectively prevented.