The invention relates to the technical field of spool inner cavity
foreign matter detection, in particular to a spool
foreign matter detection
system, device and method based on
computer vision, and the method comprises the steps: collecting an initial
image sequence of an inner cavity; extracting multi-dimensional feature vectors of the pixels; calculating a pixel-level suspicious index; calculating an average suspicious index of the suspicious areas, and generating a suspicious area
list; a self-adaptive recheck path is generated through a multi-objective optimization process; outputting a
foreign matter confidence coefficient; determining a final foreign matter confirmation index through balance
weight coefficient weighted fusion; the comprehensive
pollution index of the spool is determined, an unqualified judgment
signal is output in response to the fact that the comprehensive
pollution index is larger than a preset sorting threshold value, and otherwise a qualified judgment
signal is output; in response to manual recheck, determining that the misjudgment rate exceeds a preset misjudgment rate threshold value, and dynamically optimizing a preset sorting threshold value or a balance
weight coefficient; according to the method, the detection coverage rate and the
detection rate are remarkably improved, the effective balance between the detection speed and high precision is realized, and the detection robustness is improved.