Novel image segmentation model of multi-view sheep face for identity recognition

Traditional sheep identification is based on ear tags. However, the application of ear tags not only causes stress to the animals but also leads to loss of ear tags, which affects the correct recognition of sheep identity. In contrast, the acquisition of sheep face images offers the advantages of being non-invasive and stress-free for the animals. Nevertheless, the extant convolutional neural network-based sheep face identification model is prone to the issue of inadequate refinement, which renders its implementation on farms challenging. To address this issue, this study presented a novel sheep face recognition model that employs advanced feature fusion techniques and precise image segmentation strategies. The images were preprocessed and accurately segmented using deep learning techniques, with a dataset constructed containing sheep face images from multiple viewpoints (left, front, and right faces). In particular, the model employs a segmentation algorithm to delineate the sheep face region accurately, utilizes the Improved Convolutional Block Attention Module (I-CBAM) to emphasize the salient features of the sheep face, and achieves multi-scale fusion of the features through a Feature Pyramid Network (FPN). This process guarantees that the features captured from disparate viewpoints can be efficiently integrated to enhance recognition accuracy. Furthermore, the model guarantees the precise delineation of sheep facial contours by streamlining the image segmentation procedure, thereby establishing a robust basis for the precise identification of sheep identity. The findings demonstrate that the recognition accuracy of the Sheep Face Mask Region-based Convolutional Neural Network (SFMask R-CNN) model has been enhanced by 9.64% to 98.65% in comparison to the original model. The method offers a novel technological approach to the management of animal identity in the context of sheep husbandry.