The invention provides an
infrared near-eye
pupil detection method based on a pulse neural network, and the method comprises the steps: obtaining an
infrared near-eye image, and inputting the
infrared near-eye image into a pre-trained
pupil detection model, so as to output a
pupil position. The model takes a'
trunk-neck-detection head 'structure of YOLOv8 as a benchmark to carry out pulse reconstruction and lightweight
adaptation: a lightweight pulse
feature extraction module P-Block1 is connected in series in a
trunk network, downsampling operation is carried out, and multi-scale spatial-temporal features are extracted; in the neck network, P-Block1 is combined with up-sampling and splicing operation to replace an original feature
pyramid network, and cross-scale
feature fusion is realized. According to the invention, the pulse neural network reconstruction and lightweight design of the
system are carried out based on the YOLOv8 architecture, so that the strong
feature extraction and target detection capabilities of the original framework are inherited, and the characteristics of event driving and sparse calculation are introduced through the pulse design; the method realizes excellent balance among detection precision, model efficiency and operation
power consumption, and is especially suitable for near-eye interaction scenes with
limited resources.