Capacity Limit of Deep Learning Methods on Scenarios of Pigs in Farrowing Pen under Occlusion

Deep learning has achieved great success in computer vision. However, most of the existing methods are designed and tested on public datasets without severe occlusions, which may lead to low recall or even algorithm failure in real-world animal industry where occlusions commonly exist. Therefore, whether deep learning methods are still applicable in scenes with frequently occurring occlusions remains to be verified. This study assessed the applicability and capacity limit of deep learning methods on scenarios of pigs in farrowing pens as a case study. We collected 1,100 images from a farrowing pen where farrowing crate was adopted, and 94.8% of images have at least one piglet under occlusion. Six state-of-the-art deep learning methods in three major computer vision tasks, including object detection (Faster RCNN and Yolov4), semantic segmentation (FCN and Unet), and instance segmentation (Mask RCNN and Yolact++) were evaluated, respectively. To further challenge these methods, we randomly and gradually added virtual occlusions in ten cases on the images and tested their capacity limitation under extreme occlusion. The result showed that all the six deep learning methods achieved satisfactory performance on the original baseline case while the performance fell when occlusion became heavier. Object detection methods were less affected by occlusion than instance segmentation methods due to their rougher output. Semantic segmentation method Unet demonstrated the least effect by occlusion due to its pixel-wise classification characters, while the instance segmentation method Mask RCNN was most affected by occlusion. We suggest further study on segmentation method in animal industry.