论文标题
LAD-RCNN：牲畜面部检测和归一化的强大工具
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
论文作者
论文摘要
随着对标准化大规模牲畜农业的需求和人工智能技术的发展，对猪，牛，绵羊和其他牲畜进行了大量对动物面部识别领域的研究。面部识别包括三个子任务：面部检测，面部标准化和面部识别。大多数动物面部识别研究都集中在面部检测和面部识别上。拍照时，动物通常是不合作的，因此收集的动物面部图像通常朝着任意指示。非标准图像的使用可能会大大降低面部识别系统的性能。但是，没有关于用任意方向对动物面部图像进行标准化的研究。在这项研究中，我们开发了一个轻度的角度检测和基于区域的卷积网络（LAD-RCNN），该网络包含一种新的旋转角度编码方法，该方法可以检测一个阶段的旋转角度和动物面的位置。 LAD-RCNN在单个GEFORCE RTX 2080 TI GPU上的帧速率为72.74 fps（包括所有步骤）。 LAD-RCNN已在多个数据集上进行了评估，包括山羊数据集和监视红外图像。评估结果表明，面部检测的AP超过95％，并且在所有测试数据集上检测到的旋转角度和地面旋转角度之间的偏差小于0.036（即6.48°）。这表明LAD-RCNN在牲畜脸及其方向检测方面具有出色的性能，因此非常适合牲畜面部检测和正常化。代码可在https://github.com/sheepbreedinglab-hzau/lad-rcnn/上获得
With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48°) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/
