This invention relates to the field of
computer vision, and more particularly to a
crankshaft dimension detection method based on
computer vision. The method includes: acquiring original
crankshaft images from different
viewpoints and performing image preprocessing to extract the
crankshaft contour; identifying crankshaft contour feature points based on the contour and constructing a crankshaft contour feature
point set; calculating preliminary dimensions based on the crankshaft contour feature points; and introducing a geometric dynamic projection
correction algorithm to optimize the preliminary dimensions, obtaining optimized crankshaft dimensions. This method solves the problems of inaccurate crankshaft
dimension measurement due to projection
distortion in traditional multi-view
image acquisition processes; the inability of conventional
image processing techniques to effectively extract accurate geometric features due to uneven lighting and
noise interference on the crankshaft surface; and the failure of traditional methods to fully consider projection
distortion at each viewpoint, thus failing to achieve reasonable dimension optimization.