Abstract: In order to solve problems existing in Chan-Vese model and local binary fitting (LBF) model, such as model sensitivity to the initial contour position and running slow in the segmentation of strip steel defect image, a novel model local information-based convex active contour (LICAC) is proposed. By converting non-convex optimization problem to a convex optimization problem via convex optimization technology , and applying the Split Bregman method for fast solution，the issues of the sensitivity to the initial contour position occurring in Chan-Vese model and LBF model are solved. With introduction of the local information, the new model is efficient in the segmentation of the strip surface defect image which is non-uniform gray. By using this model to segment single-target region strip defect image, four common defect categories, including weld, rust, holes and scratches are experimented, and experimental results show that the segmentation effect and operation time of the proposed model are better than the rest two kinds. In addition, this model can also be used to segment multi-target regions defect image, four common defect categories are experimented, including scratches, inclusion, pitting, and wrinkles, and experimental results have verified the validity of the model.

Key words: Chan-Vese model, Image segmentation, Local binary fitting model, Strip steel, Surface defects