Method and system for wood identification base on depth learning of structural feature image

Abstract

Embodiments of the present invention relate to a wood identification method and system based on depth learning of structural feature images. The method comprises the following steps: acquiring wood cross-sectional structure image data; Dividing the image data into a plurality of image blocks of uniform size; Establishing a training set and a test set corresponding to the image data according to aplurality of the image blocks; Construction of Multi-Layer Convolution Neural Network for Wood Image Identification; The training set is used for deep learning of the wood image identification multi-layer convolution neural network; The depth learning model is tested by the test set, and the model parameters are optimized according to the test results, and a wood image identification depth learning algorithm model is generated. A wood image data to be discriminated is identified according to the image recognition depth learning algorithm model. Thus, the accurate and fast identification of thewood species to be identified can be realized.

Description

technical field [0001] Embodiments of the present invention relate to computer vision technology, and in particular to a wood identification method and system based on deep learning of structural feature images. Background technique [0002] With the continuous consumption of timber resources and the intensification of the contradiction between supply and demand in the market, the illegal logging and trade of timber driven by profit have seriously affected the sustainable utilization of timber resources, and at the same time constituted a huge threat to species protection and the ecological environment. The traditional wood identification method, based on the anatomical structure of wood, can only identify wood to the level of "genus", and the cycle is long, the cost is high, and it relies too much on professional wood identification personnel. Although the emerging DNA barcode technology and chemical fingerprint technology can realize the identification of wood at the "spec...

AI Technical Summary

Problems solved by technology

Extracting effective recognition features from the anatomical structure of wood is the key to wood recognition, but traditional wood image recognition methods extract features manually, and it is difficult to extract effective structural features that can identify different tree species.

At the same time, wood species have large intraspecific variability, and the anatomical structure of the same tree species usually has large variability, resulting in low accuracy of traditional wood structure image recognition methods

Therefore, it is a very complex and challenging task to identify wood species from wood structure images

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