Research Article
Detecting Dark Spot Eggs Based on CNN GoogLeNet Model
@INPROCEEDINGS{10.1007/978-3-030-72795-6_10, author={Min-lan Jiang and Pei-lun Wu and Fei Li}, title={Detecting Dark Spot Eggs Based on CNN GoogLeNet Model}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part II}, proceedings_a={SIMUTOOLS PART 2}, year={2021}, month={4}, keywords={Dark spot eggs Convolutional Neural Network GoogLeNet model HOG-SVM model}, doi={10.1007/978-3-030-72795-6_10} }- Min-lan Jiang
Pei-lun Wu
Fei Li
Year: 2021
Detecting Dark Spot Eggs Based on CNN GoogLeNet Model
SIMUTOOLS PART 2
Springer
DOI: 10.1007/978-3-030-72795-6_10
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.
