A Fine Segmentation Method for the Outer Boundary of Tire Image Steel Belt Based on Adaptive Thresholding Under Local Histogram Statistical Features

The accuracy of defect detection in various locations is directly affected by the influence of precise segmentation of radial tire images. As the need for high-quality tires has grown, so has interest in the steel belt region. This region contains a diversity of textures and is impacted by shadows, and only a rough segmentation of this region has been achieved in the available literature, but this is interfered with by other areas, resulting in unsatisfactory defect detection in this region. As a result, we present a fine segmentation method for boundary mutation that takes advantage of the projection property of the local narrow window. First, the histogram adhesion change detection algorithm is designed; next, the narrow sliding window’s histogram peak width change algorithm is designed; finally, a wide sliding window is used for vertical projection, and the absolute value of the histogram change rate at the boundary is designed to maximize the algorithm. To calculate the outside boundary of the steel belt, the difference between the three sets of coordinates is kept within a specific error range. The detection was performed on 1,000 self-constructed data sets, with an accuracy rate of 94% and a detection speed of 0.1 s The experiments show that the algorithm can segment the steel belt’s outer boundary more finely and robustly, and it has a strong segmentation effect for images with a near texture angle and tire sidewall cord angle.