Sun Ling, Liu Guiqiong, Yang Huiguo, Jiang Xunping, Liu Junrui, Wang Xu, Yang Han, Yang Shiping
Key Laboratory of Smart Farming for Agricultural Animals, Wuhan 430070, China.
Laboratory of Small Ruminant Genetics, Breeding and Reproduction, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Animals (Basel). 2023 Apr 24;13(9):1446. doi: 10.3390/ani13091446.
With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in the area of animal face detection and face identification was conducted. However, there are no specialized studies on livestock face normalization, which may significantly reduce the performance of face identification. The keypoint detection technology, which has been widely applied in human face normalization, is not suitable for animal face normalization due to the arbitrary directions of animal face images captured from uncooperative animals. It is necessary to develop a livestock face normalization method that can handle arbitrary face directions. In this study, a lightweight angle detection and region-based convolutional network (LAD-RCNN) was developed, which contains a new rotation angle coding method that can detect the rotation angle and the location of the animal's face in one stage. LAD-RCNN also includes a series of image enhancement methods to improve its performance. LAD-RCNN has been evaluated on multiple datasets, including a goat dataset and infrared images of goats. Evaluation results show that the average precision of face detection was more than 97%, and the deviations between the detected rotation angle and the ground-truth rotation angle were less than 6.42° on all the test datasets. LAD-RCNN runs very fast and only takes 13.7 ms to process a picture on a single RTX 2080Ti GPU. This shows that LAD-RCNN has an excellent performance in livestock face recognition and direction detection, and therefore it is very suitable for livestock face detection and normalization.
随着标准化大规模畜牧养殖的需求以及人工智能技术的发展，在动物面部检测和面部识别领域开展了大量研究。然而，目前尚无关于家畜面部归一化的专门研究，这可能会显著降低面部识别的性能。在人脸归一化中广泛应用的关键点检测技术，由于从不合作动物身上捕获的动物面部图像方向任意，并不适用于动物面部归一化。因此，有必要开发一种能够处理任意面部方向的家畜面部归一化方法。在本研究中，开发了一种轻量级角度检测和基于区域的卷积网络（LAD-RCNN），它包含一种新的旋转角度编码方法，能够在一个阶段中检测动物面部的旋转角度和位置。LAD-RCNN还包括一系列图像增强方法以提高其性能。LAD-RCNN已在多个数据集上进行了评估，包括一个山羊数据集和山羊的红外图像。评估结果表明，在所有测试数据集上，面部检测的平均精度超过97%，检测到的旋转角度与真实旋转角度之间的偏差小于6.42°。LAD-RCNN运行速度非常快，在单个RTX 2080Ti GPU上处理一张图片仅需13.7毫秒。这表明LAD-RCNN在家畜面部识别和方向检测方面具有优异的性能，因此非常适合家畜面部检测和归一化。
