PDF(Pubmed)
Abstract：
Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores. Our goal is to develop a computer vision model for automatically identifying specific points on the sow\'s body. The automatic identification and ability to track specific body areas, will allow us to conduct kinematic studies with the aim of facilitating the detection of lameness using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D videos images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37 in pixel, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D video image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.
摘要：
跛行影响动物的活动能力,引起疼痛和不适。早期的跛行往往由于缺乏观察而未被发现，精度,和可靠性。自动化和非侵入性系统提供精度和检测容易，并可以改善动物福利。进行这项研究是为了创建具有不同运动分数的母猪的图像和视频的存储库。我们的目标是开发一种计算机视觉模型，用于自动识别母猪身体上的特定点。自动识别和跟踪特定身体区域的能力，将使我们能够进行运动学研究，以促进使用深度学习来检测跛行。视频数据库是在养猪场上收集的，其场景旨在使母猪以不同的跛行得分进行运动拍摄。使用两个立体相机来记录2D视频图像。13名运动专家使用ZinproCorporation开发的运动评分系统对视频进行了评估。从这个带注释的存储库中，使用开源的基于深度学习的动物姿势跟踪框架SLEAP（社交LEAP估计动物姿势）对计算模型进行了训练和测试。使用LEAP架构构建性能最佳的模型，以准确跟踪6个（侧视图）和10个（背侧视图）骨架关键点。该架构实现了0.90和0.72的平均精度值，6.83和11.37像素的平均距离，外侧和背侧视图的相似性为0.94和0.86，分别。这些计算模型被提出作为精确的畜牧业工具和方法，用于自动和客观地识别和估计猪的姿势。具有不同猪运动分数的2D视频图像库可以用作教学和研究的工具。根据我们的骨架关键点分类结果，可以开发一个自动系统。这可能有助于客观评估母猪的运动分数，改善他们的福利。
