A real-time parallel combination segmentation method for aluminum surface defect images

A single defect image segmentation algorithm cannot obtain the desired segmentation results for all images because of the defect diversity. A parallel combination segmentation method is proposed to integrate multiple results of the different segmentation algorithms to obtain the desired defect segmentation map for high-speed aluminum surface defect detection. Two types of segmentation algorithms are designed in this combination framework, namely, the automatic threshold segmentation based on the image statistical model and adaptive entropy-based segmentation. The automatic threshold segmentation algorithm detects defects rapidly using the threshold parameters obtained by modeling the image effectively, and the adaptive entropy-based segmentation algorithm effectively detects defects using ID information entropy. These two types of segmentation algorithms run in parallel, and their segmentation results are fused by an "and" operation. Thus, an improved image segmentation map with higher accuracy is obtained. Many experimental results and field applications show that the parallel combination segmentation algorithm is a stable and efficient segmentation algorithm, which improves the accuracy of the original segmentation algorithm that it contains.