Abstract
Aiming at the problems of high labor intensity and low efficiency in detecting dark spot eggs, a method of detecting dark spot eggs based on GoogLeNet model is proposed. This method uses Inception convolution module in GoogLeNet model to automatically extract dark spot eggs features and realize the detection. A device for collecting transparent images of eggs was set up in the experiment, and the sample collection experiments were designed to acquire samples. A total of 1200 dark spot eggs images and 8850 normal eggs images were obtained. Selecting 1200 samples of these two kinds for network modeling. The experimental results show that the detection accuracy of dark spotted eggs based on CNN GoogLeNet model is 98.19%. In order to further verify the GoogLeNet model, this paper repeats the above experiments using the VGG16 and VGG19 models of CNN model, and compares the accuracy. To further validate the GoogLeNet model, this paper repeats the above experiments using VGG16 and VGG19 models, and compares the accuracy. The results show that the three CNN models together have high detection accuracy, and the GoogLeNet model is highest, which provides a new method for egg quality detection.
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Jiang, Ml., Wu, Pl., Li, F. (2021). Detecting Dark Spot Eggs Based on CNN GoogLeNet Model. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_10
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DOI: https://doi.org/10.1007/978-3-030-72795-6_10
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