Image stitching method, apparatus and device based on reinforcement learning and storage medium

An image stitching method based on reinforcement learning, comprising the following steps of:

• acquiring initial calibration parameters through a calibration board arranged on a motion platform, and collecting a sample image of a detected sample on the motion platform at each moment and position information of the motion platform at each moment in a movement process of the motion platform;
• setting a negative reward function based on image stitching quality at each moment by taking the image stitching quality at each moment and the position information of the motion platform at each moment as a state at each moment and a calibration parameter adjustment amount at each moment as an action at each moment;
• randomly generating an action set, and acquiring a state set and a negative reward value set according to the action set, the initial calibration parameters, the position information of the motion platform at each moment and the negative reward function;
• constructing a Markov experience sequence according to the action set, the state set and the negative reward value set, and constructing a probability kinematics model through the Markov experience sequence, wherein the probability kinematics model is used for predicting an occurrence probability of a state at the next moment according to a state and action at a current moment;
• constructing a state value function based on an occurrence probability of the state at each moment and a negative reward value at each moment, and acquiring an optimal action at each moment by optimizing the state value function;
• and
• acquiring optimized calibration parameters at each moment through the optimal action and the initial calibration parameters, and carrying out image stitching on corresponding sample images through the optimized calibration parameters at each moment.