Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning
Martin Riekert, Svenja Opderbeck, Andrea Wild, Eva Gallmann
Abstract
Continuous monitoring of pig posture is important for better understanding animal behavior. Previous studies focused on day recordings and did not investigate how deep learning models could be applied during longer periods including night recordings under near-infrared light from several pens. Therefore, the objective of this research was to study how a suitable deep learning model for continuous 24/7 pig posture detection could be achieved. We selected a deep learning model from over 150 different model configurations covering experiments concerning 3 detection heads, 4 base networks, 5 transfer datasets and 12 data augmentations. For this purpose, we test and validate our models using 4690 annotations of randomly drawn images from 24/7 video recordings covering 2 fattening periods from 10 pens. Our results indicate that pig position and posture was detected on the test set with 84% [email protected] (49% [email protected][0.50:0.05:0.95]) for day recordings and for night recordings 58% [email protected] (29% [email protected][0.50:0.05:0.95]) was achieved. The main reason for lower mAP during night recordings was degraded near-infrared image quality. Our work reports important findings concerning the applicability of deep learning models on night near-infrared recordings for posture detection. The dataset is publicly available for further research and industrial applications.