The invention belongs to the technical field of
information storage, and particularly provides a self-adaptive
fluorescence signal reading method based on a
convolutional neural network, which comprises the following steps: S1, hybridizing
a DNA decoding chain with a coding chain in a micro
pool of a storage substrate to obtain a primary image group of the hybridized storage substrate, and marking a
fluorescence probe at the
tail end of the decoding chain; s2, segmenting the primary image according to a micro-
pool unit, extracting a plurality of sub-images, and performing data enhancement to obtain a secondary image; s3, marking the secondary image; s4, dividing the
data set into a
training set and a
verification set; s5, inputting a
training set into the CNN model for training, and assisting dynamic adjustment and optimization of hyper-parameters; and S6, applying the trained CNN model to test a secondary image, and outputting a
fluorescence recognition result. According to the method, the judgment standard can be automatically learned according to a large number of training samples, the fluorescence
signal can be accurately, stably and efficiently read, manual threshold value presetting is not needed, the influence of interference factors is small, and the robustness and accuracy of
DNA information storage can be effectively improved.