Generation Algorithm Of Dairy Goat Images Based On Normalized-self-attention GANs

In recent years,the use of deep learning to complete object detection and object tracking(such as dairy goat target tracking)in the field of livestock industry for achieving intelligent management has gradually become popular.But a large number of training images are generally needed in deep learning,otherwise the generalization ability and robustness of networks will be poor.In view of the insufficient number of existing dairy goat image for training,based on the five types of dairy goat images collected from dairy goat breeding bases of Northwest Agriculture & Forest University,this article uses the semi-supervised generative adversarial network introducing training labels during the training process to generate dairy goat images with specified labels,which can achieve data augment and generated results can be applied in the further supervised learning.The research contents and results in this article are as follows:(1)Based on characters of dairy goat images for five classes,the one-hot training label generally used in the semi-supervised generative adversarial network is replaced with multi-label which can improve the quality of generated images by adding more supervised information into the network.(2)Aiming at the problem that generative adversarial network perform weak in the details of generated images,this article adds the improved self-attention mechanism to the generative adversarial network.By capturing more long-range dependencies,the defect caused by local receptive field in the convolution networks can be avoided.And by normalizing the feature vectors before calculating self-attention matrix,the errors caused by noises can be eliminated as much as possible.(3)In addition,in order to avoid some conventional problems during the process of training generative adversarial network like hard converge and model collapse,this article uses the wasserstein distance with penalty to replace the original Min-Max entropy loss and calculating classification loss function of network on the basis of the multi-label.In order to verify the effectiveness of the proposed algorithm,this article trains different networks on the public human face dataset named celeb A and compares their results.The final experimental results show that when using FID and IS algorithms for evaluation,the quality of human face images generated by the Improved-Self-Attention GANs is best in comparison with other networks.In addition to the FID and IS algorithms,this article also put forward the SSIM-Mean algorithm based on the SSIM algorithm to evaluate the generated dairy goat images.The experimental results reveal that no matter which of the three types of algorithms is used as metrics the Improved-Self-Attention GANs proposed in this article can generate higher-quality dairy goat images.