Research on Defect Recognition Algorithm of Wood Veneer Based on Image Threshold Optimization and Sliding Window Processing

Adressing the detection and localization of defects in wood veneer, a binary image detection combined with region growing method with sliding window and an image threshold segmentation method by using the genetic algorithm is proposed. The overall procedure consists of three steps. Firstly, denoising and grayscale processing of the color wood veneer image according to its features; secondly, setting the adaptation index and thresholds the veneer image combined with genetic algorithm to separate the defective part from the background part; finally, using the sliding window and region growing method to locate the defects. The detection experiments in the spruce wood image dataset is carried out. The experiment results show that the defect detection algorithm proposed in this paper can more accurately detect various types of veneer defects, and can meet the subsequent veneer digging and patching processing requirements.