Image segmentation using fuzzy min-max neural networks for wood defect detection

In this work a colour image segmentation method for wood surface defect detection is presented. In an automated visual inspection system for wood boards, the image segmentation task aims to obtain a high defect detection rate with a low false positive rate, i.e., clear wood areas identified as defect regions. The proposed method is called FMMIS (Fuzzy Min-Max neural network for Image Segmentation). The FMMIS method grows boxes from a set of seed pixels, yielding the minimum bounded rectangle (MBR) for each defect present in the wood board image. The FMMIS method was applied to a set of 900 colour images of radiata pine boards, which included 10 defect categories. The FMMIS achieved a defect detection rate of 95 percent on the test set, with only 6 percent of false positives. The area recognition rate (ARR) criterion was computed, to measure the segmentation quality, using as a reference the manually placed MBR for each defect. The ARR achieved 94.4 percent on the test set. The results show significant improvements compared with previous work and that the computational load of FMMIS is suitable for real-time segmentation tasks.