A new method for defect detection in lumber images: Optimising the energy model by an irregular parametric genetic approach

Accurate detection of surface defects plays a vital role in automated analysis of lumber quality in the wood industries. A new method, based on a genetic optimisation of energy model, is introduced here for defect detection in lumber images. In this method, a hypothesis testing framework is defined, first to separate defects from natural tissue of lumber. Then, the boundary of lumber is estimated by a decision function based on the energy optimisation method which is driven by an irregular parametric genetic approach. Performance of the proposed algorithm was evaluated on real captured lumber images containing several types of surface defects. The results demonstrate that the proposed method extracts the defects approximately 4.4% better than its alternatives while at the same time decreasing false detections by approximately 5.4%. The results obtained also show considerable improvements in accuracy and precision of the proposed method compared to other examined methods, especially when a low false detection (i.e. at least 10%) is desired.