基于深度学习的木材缺陷图像的识别与定位

传统的木材缺陷定位方法主要有物理设备检测和传统计算机技术检测，但这两种方法均存在数据收集困难、高度依赖数据本身等问题，不适用于实际生产。本文提出一种基于深度学习的自动缺陷定位模型（Automatic defect location model, ADLM），包含单缺陷定位模型（Single defect location model, SDLM）与多缺陷定位模型（Multi-defect location model, MDLM），满足不同需求。模型使用MobileNet作为骨干网，只需少量数据集进行训练。在公开数据集Wood Defect Database中，该模型可获得86.1%的缺陷识别率。在单缺陷数据集中，该模型可获得97.5%的定位精确率。在多缺陷数据集中，该模型可获得90.0%的定位精确率。与传统的木材缺陷识别模型相比，基于深度学习的自动缺陷定位模型无须前期人工提取特征，具有检测速度更快、精准度更高以及适用性更广等优点。

Recognition and Localization of Wood Defect Image Based on Deep Learning

The traditional wood defect location methods mainly include physical equipment detection and traditional computer technology detection, but they are difficult to collect data, highly dependent on the data itself, which are not suitable for actual production. We propose an automatic defect location model (ADLM) based on deep learning, which includes single defect location model (SDLM) and multi-defect location model (MDLM) to meet different requirements. This model uses MobileNet as the backbone network, and only a few data sets are needed for training. In the public data set Wood Defect Database, this model has a defect identification rate of 86.1%.In the single defect data set, positioning accuracy of the model can achieve 97.5%. In the multi-defect data set, positioning accuracy of the model can achieve 90.0%. Compared with the traditional model, the ADLM need not manual feature extraction at the early stage, and has the advantages of faster detection speed, higher accuracy and wider applicability.