基于DAM-ResNet的广视野下牛舍奶牛个体识别方法

Individual Identification Method of Dairy Cows in Cowsheds under Wide Field of View Based on DAM-ResNet

摘要:

针对牛舍内广视野下奶牛图像存在遮挡、变形较大而造成个体识别困难等问题，提出了一种基于DAM-ResNet模型的广视野下牛舍奶牛个体识别方法。采用Mask R-CNN模型对奶牛背部、躯干左侧、躯干右侧以及臀部4类奶牛部位进行分割。在ResNet34残差网络的基础上引入第二代可变形卷积，以加强对分割结果中奶牛花纹变形的图像特征提取；将AFF注意力特征融合模块融入到残差结构中，实现对远距离小目标奶牛图像的准确识别；采用细粒度分类损失函数互通道损失MC-Loss提高模型对花纹相似奶牛的识别准确率。利用奶牛分割后的图像构建奶牛多部位数据集，并训练DAM-ResNet模型。对57头奶牛共12864幅图像数据集进行了奶牛个体识别测试，结果表明，奶牛背部、躯干左侧、躯干右侧、臀部识别准确率分别为96.13%、96.56%、96.94%、93.14%，比ResNet原模型分别提高2.76、2.88、2.92、4.25个百分点。本文方法在10~20m、20~30m和30~40m范围内识别准确率分别为97.54%、90.72%和82.17%。研究结果可为奶牛智能化养殖提供技术支持。

Abstract:

Aiming at the problems such as occlusion and large deformation of cow images in the cowshed under a wide field of view, which cause difficulties in individual identification, a method for individual identification of cows in the cowshed under a wide field of view based on the DAM-ResNet model was proposed. The Mask R-CNN model was used to segment four types of dairy cow parts: the back, the left side of the trunk, the right side of the trunk and the buttocks. Based on the ResNet34 residual network, the second-generation deformable convolution was introduced to enhance the extraction of deformed image features of cow patterns in the segmentation results. Integrating the AFF attention feature fusion module into the residual structure to achieve accurate recognition of images of small target cows at a distance. The fine-grained classification loss function, interchannel loss (MC-Loss), was adopted to improve the recognition accuracy of the model for cows with similar patterns. The multi-part dataset of dairy cows was constructed by using the segmented images of dairy cows, and the DAM-ResNet model was trained. Individual cow recognition tests were conducted on a dataset of 12864 images of 57 dairy cows. The results showed that the recognition accuracy rates of the back, left trunk, right trunk and buttocks of dairy cows were 96.13%, 96.56%, 96.94% and 93.14%, respectively, which were 2.76, 2.88, 2.92 and 4.25 percentage points higher than those of the original ResNet model. The recognition accuracy rates of the method proposed within the ranges of 10~20m, 20~30m and 30~40m were 97.54%, 90.72% and 82.17%, respectively. The research results can provide technical support for intelligent dairy cow breeding.