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Miaomiao Wei

at Zhongyuan Univ of Technology

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Proceedings Article | 3 January 2020 Paper

Sunflower seed sorting based on Convolutional Neural Network

Zhengguang Luan, Chunlei Li, Shumin Ding, Miaomiao Wei, Yan Yang

Proceedings Volume 11373, 113731K (2020) https://doi.org/10.1117/12.2557789

KEYWORDS: Convolution, Convolutional neural networks, Data modeling, Image classification, Neural networks, Feature extraction, Network architectures, Image enhancement, Library classification systems, Lithium

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Crop seeds sorting is an important task for industrial production. In the sunflower seed production processing, a large number of impurities are mixed into the seeds. Besides, the image of sunflower seeds has the characteristics of random distribution, various kinds of bad sunflower seeds. In addition, the distinction between the bad seeds and normal seeds is not obvious. the recognition rate of traditional methods suffer from a low accuracy is not meeting the actual requirements. In the past few years, many methods based on convolutional neural network (CNN) has made great success in object detection and recognition. However, these networks have a large model size. In this paper, we developed a CNN model with eight convolutional layers to extract the image feature and a skip connection is used to increase the learning ability of the model. Compared with the classical convolution network, it has smaller size without reducing the accuracy. However, because of the CNN features obtained by the convolutional layers are seldom investigated due to their high dimensionality and lack of global representation. Therefore, we introduced a channel attention mechanism adaptive to recalibrate the channel-wise features by considering dependencies among feature channels to strengthen the image features that are import to the classification tasks. Extensive experiments show that our model with attention mechanism achieves better accuracy on the sunflower seed images dataset compared with several classical networks.

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