Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction

Abstract

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting from the low threshold is taken as the marker image, and that from the high threshold is taken as mask image, and mathematical morphology reconstruction is performed on the two images, with the resulting image being the preliminary result of the wheel defect segmentation. Finally, with area and diameter parameters as the conditions, the preliminary segmentation result is analyzed, and the defect regions satisfying the conditions are taken as the ultimate result of the whole solution. Experiments proved the feasibility of the above solution, which is found capable of extracting different types of wheel defects satisfactorily.

Nondestructive Testing in Composite Materials

“…The paper by Zhang et al [16] is concerned with a technical solution that combines the adaptive threshold segmentation algorithm and the morphological reconstruction operation to extract the defects on wheel X-ray images. The obtained results show that this method is capable of accurate segmentation of wheel hub defects.…”

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

“…References [36,37] indicated that gray-level morphological reconstruction (GMR) can be used to extract objects, which are connected components with the same intensity value and larger (smaller) than the intensity value of the external boundary pixels, while keeping their intensity, shape, and contour detail information and suppressing trivial background clutter and noise. Motivated by this vision, as analyzed in above Section 2.1 part, since the ship target is also a small uniform region with higher (lower) contrast compared with its surrounding backgrounds in TIR remote sensing image, we adopt morphological reconstruction [38] on gray-level infrared images in a dual method to highlight ship targets and remove sea clutter simultaneously.…”

Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable

“…Many nondestructive testing methods are available, such as visual inspection [1,2], radiography [3], computed tomography [4], acoustic emission monitoring [5], ultrasound [1,6], and magnetic flux leakage [7]. However, many of these methods have limitations when applied to multi-wire cables.…”