Research on Adaptive Fast Threshold Segmentation Algorithm for Surface Defect Detection of Wood-Based Panel

Objective: An adaptive fast thresholding image segmentation algorithm was proposed in this paper, which could quickly and accurately separate the defects from the surface images of wood-based panels, and provide support for on-line detection of wood-based panel surface defects. Method: Firstly, the algorithm divided the whole image into several sub-regions. Secondly, the defect areas were located by calculating the variance of each sub-region. And then, the image segment was only done in defect areas for solving the problem of accurate segmentation of small targets. The one-dimension gray scale histogram of extracted defect area was processedusing histogram smoothing to remove the non-significant peaks. According to the main wave peaks reserved in the histogram after the processing, the number of the thresholds and segmentation interval for each threshold were determined adaptively. At each interval, the threshold was searched using an improved fast Otsu segmentation algorithm. Through the analysis of the Otsu algorithm, the threshold was found by a conditional search instead of the exhaustive search and the search direction was specified. In each segmentation interval, the improved fast Otsu segmentation algorithm was used to search the threshold, which improved the search speed. Result: The segmentations of surface images of wood-based panels with five types of defects such as oil stains, big wood shavings, glue spots, sundries and loose regions were done using the adaptive fast algorithm proposed in this paper. Although the number and type of the defects were not fixed, this algorithm still could determine the number of the thresholds automatically. All kinds of defects were separated from the surface images in 15 ms with a above 97% segmentation accuracy rate. Conclusion: The adaptive fast threshold segmentation algorithm presented in this paper can quickly and accurately separate the defects from the surfaces of the panels, and the execution speed and the segmentation effect meets the requirements of the on-line defect detection system. It provides a new approach for automatic on-line detection of surface defects on wood-based panels. © 2018, Editorial Department of Scientia Silvae Sinicae. All right reserved.