Introducing a new Workflow for Pig Posture Classification based on a combination of YOLO and EfficientNet

Witte, Jan-Hendrik
Marx Gómez, Jorge
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This paper introduces a pipeline for image-based pig posture classification by applying YOLOv5 for pig detection and EfficientNet for subsequent pig posture classification into 'lying' and 'notLying'. A high-quality dataset consisting of 5311 heterogeneous images from different sources with 78215 bounding box annotations was created. The bounding box annotations were then used to create a separate dataset for image classification, consisting of 9209 and 7855 images for each 'lying' and 'notLying'. The YOLOv5 model achieves an AP of 0.994 for pig detection, while EfficientNet achieves a precision of 0.93 for pig posture classification. Comparing the results of the proposed method with other approaches found in literature, it shows that significant improvements in terms of accuracy can be achieved by splitting the classification of pig posture into separate models. This research provides a foundation for the continued development of real-time monitoring and assistance systems in pig Precision Livestock Farming.
Analytics and Decision Support for Green IS and Sustainability Applications, computer vision, deep learning, posture classification, precision livestock farming
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