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Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network
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AbstractThe presence of corn unsound kernels, one of the world's major food crops, could have a significant impact on the food industry and the food supply. The traditional method of detecting unsound corn kernels by hand during grain acquisition has many drawbacks, and computer vision‐based detection methods have become mainstream. In this paper, a corn unsound kernel detection algorithm based on generative adversarial network (GAN) sample enhancement and an improved lightweight network is introduced. The article first builds a corn unsound kernel image acquisition platform and makes a dataset by preprocessing and segmenting the collected corn seed cluster images with the improved concave point segmentation algorithm. Then, to increase the diversity and number of datasets, the StyleGANv2 network was improved to generate corn‐unsound kernel images with diverse features. Finally, to meet the demand for lightweight detection, the MobileVit network was optimized to improve the network's recognition accuracy, which reached 96.2%. The article verifies the effectiveness and superiority of the proposed algorithm through experiments.
Title: Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network
Description:
AbstractThe presence of corn unsound kernels, one of the world's major food crops, could have a significant impact on the food industry and the food supply.
The traditional method of detecting unsound corn kernels by hand during grain acquisition has many drawbacks, and computer vision‐based detection methods have become mainstream.
In this paper, a corn unsound kernel detection algorithm based on generative adversarial network (GAN) sample enhancement and an improved lightweight network is introduced.
The article first builds a corn unsound kernel image acquisition platform and makes a dataset by preprocessing and segmenting the collected corn seed cluster images with the improved concave point segmentation algorithm.
Then, to increase the diversity and number of datasets, the StyleGANv2 network was improved to generate corn‐unsound kernel images with diverse features.
Finally, to meet the demand for lightweight detection, the MobileVit network was optimized to improve the network's recognition accuracy, which reached 96.
2%.
The article verifies the effectiveness and superiority of the proposed algorithm through experiments.
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