面向边缘计算的轻量级母猪分娩识别模型

Recognizing sow parturition using lightweight model with edge computing

为实时监测母猪分娩过程并准确分析记录其完整产程的产仔数、产仔间隔和产程等信息,该研究运用知识蒸馏和剪枝,结合了ResNet50高准确率和MobileNetV3高检测效率的优势设计了一种轻量级网络。采用数据增强提高教师模型ResNet50对分娩特征的提取能力,通过掩模生成蒸馏(maskedgenerativedistillation,MGD)提高学生模型MobileNetV3-S对分娩关键区域的表达能力,并通过依赖关系图(dependency graph)显式建模学生网络层间的依赖关系,结合分组耦合参数对学生模型进行剪枝。剪枝得到的MobileNetV3-S(MGD)_Prune参数量为0.74 M,在DELL OptiPlex微型机上检测速度为83.10帧/s,单栏视角测试准确度为91.48%,相比于ResNet50的检测速度提升了67.13帧/s,测试准确度下降0.98个百分点。试验结果表明,单栏视角对监测母猪分娩更为有效,模型对于产仔平均间隔的检测误差为0.31 s,仔猪出生事件的平均持续时长检测误差为0.02 s,能够高效监测母猪分娩全过程。

The reproductive performance of sows can play a critical role in animal breeding,particularly in the efficiency and effectiveness of selection.However,manual recordings of piglet births and their survival rates cannot fully meet the large-scale production in recent years.The high precision is often required to capture more nuanced data,such as the intervals between births.The advanced technologies can be expected to enhance both the accuracy and efficiency of animal breeding programs.In this study,a lightweight network was developed to rapidly and accurately monitor the sow birthing in real time.Specifically,essential birthing metrics were engineered to analyze,such as the number of piglets born and the precise intervals between each birth.The lightweight network was tailored for the real-time monitoring of sow birthing activities.The critical birthing parameters were obtained to significantly enhance the efficiency and accuracy of breeding programs.Initially,the efficacy of different monitoring views-specifically,single versus double-column views-were evaluated on the accuracy of the improved model.A single-column view was significantly improved to accurately monitor the birthing events.The real-time decision-making and direct implications were obtained from the breeding outcomes.Advanced video processing techniques were incorporated,such as horizontal and vertical flipping.Some challenges were remained on the dynamic changes in the sow posture and varying camera perspectives during monitoring.Moreover,different lighting conditions were adapted to capture the inherent motion blur of active piglets during birth.Color jittering and Gaussian blur were then employed to significantly enhance the robustness of the model.The reliable performance was obtained under diverse operational conditions.Further advancements were achieved through a comparative analysis of classification networks.The results revealed that ResNet50 was greatly contributed to the recognition accuracy.MobileNetV3-S was performed the best with the compact model size and superior processing speed of 505.14 frames per second,indicating the optimal operational efficiency.Furthermore,MobileNetV3-S was refined to apply with the masked generative distillation-a sophisticated technique that was effectively enhanced the network's ability to capture and interpret essential birthing features.ResNet50 was utilized as the teacher model in the practical application,while MobileNetV3-S as the student model.The training was conducted using masked generative distillation followed by dependency graph pruning.The tests were carried out on a DELL OptiPlex microcomputer.An impressive detection speed of 83.10 frames per second was achieved with a test accuracy in a single-column field of view of 91.48%.Although there was a slight decrease in the accuracy of 0.98 percentage points,the detection speed was improved by 67.13 frames per second.This improved model was then deployed at the edge for testing.The better performance was achieved in the managed farrowing intervals with a detection error of just 0.31 seconds and the duration of piglet birth events with a mere 0.02-second error.Highly efficient and exceptionally precise real-time monitoring was obtained to promote the management practices of breeding activities in complex farm environments.In conclusion,the advanced computational techniques were integrated for the transformative potential to the monitoring of sow birthing.Real-time data was acquired to combine the image processing and machine learning.Some standards can be offered for the accuracy and efficiency in livestock management.The reproductive dynamics can greatly contribute to the sustainable and scientifically-informed animal husbandry.