The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. ![]() To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. ![]() Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value.
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