A Cotton and Flax Fiber Classification Model Based on Transfer Learning and Spatial Fusion of Deep Features

Abstract

In order to make up the disadvantages of transfer learning methods, a cotton and flax fibred classification model with fusion of deep features transfer learning is proposed. First, the proposed model utilizes VGG16 and InceptionV3 to extract deep features from cotton and flax fiber images. Next, using spatial feature fusion algorithm, the model merges the deep features extracted from different networks. Finally, the generalized deep feature is used to train SoftMax classifier, thereby achieving accurate detection of cotton and flax fibers. In testing datasets which have 4008 images, the cotton and flax Fiber classification accuracy, sensitivity and specificity of the proposed model are 0.978, 0.969, 0.985 respectively. The experiments demonstrate that the proposed model outperforms the state-of-the-art model in the same hardware environment. The results show that the proposed model can detect cotton and flax Fiber with high accuracy.