Adjacent age classification algorithm of yellow-feathered chickens based on multi-scale feature fusion

• Multi-scale feature extraction was introduced to extract features from yellow-feathered chicken face image. • We built a database for age classification of yellow-feathered chickens. • Using our method, the identification accuracy of adjacent age of yellow-feathered chickens is 96.29 %. The age of yellow-feathered chicken is important to distinguish the freshness of meat quality in the trade of yellow-feathered chicken. To investigate whether the Convolutional Neural Network (CNN) model can be applied to the instar classification of yellow-feathered chickens, a multi-scale feature fusion model called Chicken_Age_Network (CANet) was proposed. The model uses Inception to construct a feature extraction layer and extract the feature information of each chicken face image to improve the classification accuracy of the model. First, the self-developed yellow-feathered chicken facial image collection application was used to collect images, and the yellow-feathered chicken image database was constructed by using day-age classification. Second, the standard face image of yellow-feathered chicken was obtained by using Structural Similarity Index Method detection(SSIM), image segmentation, background removal, and normalization. The adjacent age classification needs to extract more features, and CANet’s feature extraction layer based on multi-scale feature fusion can extract features of different sizes. Another advantage of CANet is that the GMP (Global Max Pooling) replaces the final fully connected layer of the general CNN to reduce parameters and optimize the network model. Finally, chicken face images of adjacent days of age were tested and compared on VGG13, VGG19, DenseNet121, DenseNet161, SE-ResNet-20, MobileNet V1, ShuffleNet G2, ResNet50, ResNet34 and CSPDenseNet121. Test results show that CANet can quickly and accurately identify the age of yellow-feathered chickens. The classification accuracy on CANet model is 96.29 %, which is better than VGG13 (93.09 %), VGG19 (95.08 %), ResNet50 (85.53 %), ResNet34 (91.14 %), DenseNet121 (93.03 %), DenseNet161 (93.33 %), MobileNet V1(86.06 %), ShuffleNet G2(93.18 %), CSPDenseNet121(84.76 %) and SE-ResNet-20 (83.86 %). In order to verify the generalization of the model, experiments were carried out on the public data set CIFAR-10. Results show that CANet has good generalization and can be applied to other classification problems.