An Improved Wood Recognition Method Based on the One-Class Algorithm

Abstract

Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization.

Leaves and Twigs Image Recognition Based on Deep Learning and Combined Classifier Algorithms

“…Our proposed method needs less equipment to collect the leaf images and also does not need to apply complicated and multiple preprocessing jobs compared to classifications based on satellite images and other tree features, such as wood and barks [21,52]. The leaf image collection in this study only required the use of high branch shears to collect leaves and smartphones to capture the leaf images.…”