基于卷积神经网络模型的裂纹鸡蛋图像识别

目的:提高基于计算机视觉的鸡蛋裂纹检测方法的准确性和运行效率。方法:使用禽蛋模拟撞击设备得到裂纹鸡蛋，并通过鸡蛋动态图像采集设备采集不同角度裂纹鸡蛋和完好鸡蛋图像，然后以原始图像和经预处理后图像分别建立用于裂纹鸡蛋图像识别的YOLO-v5、ResNet和SuffleNet模型，并比较不同模型识别准确度以及对未经预处理图像的适应性。结果:YOLO-v5、ResNet和SuffleNet模型均可有效识别经过预处理的裂纹鸡蛋图像，其验证集准确率分别为98.8%,97.8%,99.4%。对于未经预处理的裂纹鸡蛋，ResNet模型判别准确率较低，而SuffleNet模型对其适应性较好，判别准确度超过99%。结论:在卷积神经网络模型中，SuffleNet模型适用于裂纹鸡蛋图像的识别，且采集的图像无需进行预处理。

Research on image recognition of cracked eggs based on convolutional neural network model

Objective: In order to improve the accuracy and operating efficiency of the egg crack detection method based on computer vision. Methods: Used poultry egg simulation impact equipment to obtain cracked eggs, and collected images of cracked eggs and intact eggs from different angles through egg dynamic image acquisition equipment. Then, the YOLO-v5, ResNet and SuffleNet models for cracked egg image recognition were established for the original and the preprocessed egg images, respectively. After that, the recognition accuracy and the adaptability for original egg images recognition of different models were compared. Results: The YOLO-v5, ResNet and SuffleNet models could effectively identify the preprocessed cracked egg images, and the accuracy rates of verification set were 98.8%, 97.8% and 99.4% respectively. For original eggs images, the ResNet model had a low recognition accuracy, while the SuffleNet model had the highest recognition accuracy, which was up to 99%. Conclusion: Among the convolutional neural network models, SuffleNet model is most suitable for cracked egg image recognition, and the egg image preprocess is not necessary. This study provides a reference for the further improvement of crack egg detection methods based on computer vision.