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基于YOLOv4的猪只饮食行为检测方法

Pig Diet Behavior Detection Method Based on YOLOv4

  • 摘要: 针对猪舍环境下猪只饮食行为自动化检测程度较低的问题,提出了一种基于YOLOv4的猪只饮食行为检测模型。基于多时间段、多视角和不同程度遮挡的猪只饮食图像,建立了猪只饮食行为图像数据库,利用YOLOv4深度学习网络的深层次特征提取、高精度检测分类特性,对猪只饮食行为进行检测。结果表明,基于YOLOv4的猪只饮食行为检测模型在不同视角、不同遮挡程度以及不同光照下均能准确预测猪只的饮食行为,在测试集中平均检测精度(m AP)达到95.5%,分别高于YOLOv3、Tiny-YOLOv4模型2.8、3.6个百分点,比Faster R-CNN模型高1.5个百分点,比Retina Net、SSD模型高5.9、5个百分点。本文方法可为智能养猪与科学管理提供技术支撑。

     

    Abstract: It is of vital significance to detect pig’s eating and drinking behavior by using intelligent method,and analyze the law of eating and drinking water,which plays an important role in early warning of pig disease and maintaining pig welfare. Pig diet behavior detection model based on YOLOv4 was proposed. Aiming at the pig diet image with multi time period,multi view angle and different degrees of occlusion,the database of pig eating behavior image was established. The in-depth feature extraction and high-precision detection classification characteristics of YOLOv4 deep learning network were used to accurately detect pig eating behavior. The results from the whole experiments showed that the model based on YOLOv4 can accurately predict the diet behavior of pigs in different angles of view,different degrees of occlusion and different illuminations. The average detection accuracy( m AP) was 95. 5%,which was 2. 8 percentage points and 3. 6 percentage points higher than that of the same series of YOLOv3 and Tiny-YOLOv4 models,1. 5 percentage points higher than that of Faster R-CNN model,5. 9 percentage points higher than that of RetinaNet model and 5 percentage points higher than that of SSD model. This method can accurately predict the occurrence of pig eating behavior and provide targeted and adaptive technical support for pig intelligent breeding and management.

     

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