Main content area

Down image recognition based on deep convolutional neural network

Yang, Wenzhu, Liu, Qing, Wang, Sile, Cui, Zhenchao, Chen, Xiangyang, Chen, Liping, Zhang, Ningyu
Information processing in agriculture 2018 v.5 no.2 pp. 246-252
data collection, image analysis, models
Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN). To deal with the above problems, a Deep Convolutional Neural Network (DCNN) for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.