This invention proposes an
adaptive imaging adjustment method for UAV inspection of port equipment, belonging to the field of imaging adjustment technology. Addressing the problems of existing technologies, such as inability to
handle complex lighting environments, difficulty in dynamically optimizing polarization angle adjustment, and the separation of
image quality evaluation and imaging parameter adjustment, lacking a closed-
loop control mechanism, this invention constructs a lightweight
convolutional neural network image quality evaluation model driven jointly by
gradient magnitude and information entropy. It defines a three-dimensional
state space including
exposure parameters, polarization angle, and
quality score, and five discrete adjustment actions. A deep Q-network is used to
train the model to output the optimal
adjustment action, which is then executed in closed-loop iterative control during UAV online inspection, supplemented by a maximum iteration count guarantee and a threshold adaptive adjustment mechanism. This invention improves the contrast and detail
clarity of microcrack imaging in
metal structures under complex lighting conditions, and enhances the
automation and robustness of imaging parameter adjustment.