Joint loss optimization based high similarity identification for milch goats
Objective: It is essential for the quick response in tracking information of animals for intelligent agriculture and animal husbandry nowadays. Individual identification of animals has been one of the challenging issues in real-time monitoring. Different from traditional methods with high harmfulness such as imprinting, our deep learning based method is adopted to implement image recognition for the several of animal and human as well as the unclear multi-features relationship. Method: First, our computer vision method is demonstrated for individual recognition of dairy goat based on deep learning. The 26 goats' pictures-oriented are acquired including the head and other parts. Fancy fancy principal components analysis (PCA) is adopted as the data expansion methods to expand the dataset. A sum of 1 040 goats' images are randomly selected for training, and 260 images are used as independent test sets; single shot MultiBox detection (SSD) network based dataset preprocessing is initial to be required. Our demonstration uses the siamese network for preliminary learning. The network structure and learning rate optimization algorithm are employed to adjust the parameters but not suitable for individual identity classification. It verifies the goat itself in terms of the highly similar data set. The effect of whole goat image is better than single head image. This obtained result has been greatly improved from the training of original head to the whole body in the context of is the solo Triplet-Loss function. The original image input of the Triplet-Loss function is composed of three pictures. Because the dairy goat is proven to have high similarity of individual based on the siamese network, it is not required to conduct the data sets integration derived of the Triplet-Loss function as well as the set of different goats images is complicated based on manual method. Next, Triplet-Loss function in dataset has its potentials compared with the siamese network method. Our joint loss function and transfer learning model residual neural network(ResNet18) obtain the goat information in terms of deep network structure. Finally, the joint loss function takes Adam as the optimizer algorithm, our demonstration can get qualified recognition as the hard batch (difficult triplets) of Triplet-Loss function are not required in the context of Triplet-Loss function and CrossEntropy-Loss function and related parameters. In addition of goats, we use the Triplet-Loss function and siamese network to option the feature for the goat face region and the whole goat region verifies the features of the goat face region recognition is not good in terms of high accuracy rate. Our illustration is not only uses you only look once (YOLOv3) network and siamese network to identify goats, but also uses transfer learning model to learn. The siamese network verifies that the goat itself based on high similarity data set, and the Triplet-Loss function and CrossEntropy-Loss function are used as final loss function to verify the effectiveness of the method. Result: The SSD network was used to preprocess the dataset. The demonstrated results illustrate that the accuracy can be improved from 86% to 93.077% by combining the joint loss function with Adam algorithm. When the joint loss function is used with Adam optimization algorithm as well as the joint loss function accounts for a certain proportion, other correlation can be realized by adjusting the parameters, the result will be obtain better recognition effect. Just compared with 74.615% of Triplet-Loss function and 89.615% of CrossEntropy-Loss function, the highest recognition accuracy is 93.077%. Conclusion: Our higher recognition effect of goat analysis is based on the model of deep learning. These goats cannot just get more effective facial features recognition but can obtain higher accuracy derived of the whole goat body. Intelligent research of individual goat should be conducted based on the segmented attribute of each part of the goat body. Furthermore, our research can lower high labor costs issues based on deep learning model in terms of computer vision archives.
Journal of Image and Graphics
Shang, C., Wang, M., Ning, J., Li, Q., Jiang, Y., & Wang, X. (2022). Joint loss optimization based high similarity identification for milch goats. Journal of Image and Graphics, 27 (4), 1137-1147. https://doi.org/10.11834/jig.200619