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

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

4 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2021 ASABE Annual International Virtual Meeting
PublisherAmerican Society of Agricultural and Biological Engineers
Pages1917-1924
Volume3
ISBN (Print)9781713833536
Publication statusPublished - Jul 2021

Publication series

NameAmerican Society of Agricultural and Biological Engineers Annual International Meeting, ASABE

Conference

Title2021 American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE 2021)
LocationVirtual
Period12 - 16 July 2021

Abstract

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.

Research Area(s)

  • Computer vision, instance segmentation, object detection, occlusion, semantic segmentation

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Capacity Limit of Deep Learning Methods on Scenarios of Pigs in Farrowing Pen under Occlusion. / Huang, Endai; Mao, Axiu; Ceballos, Maria Camila et al.
2021 ASABE Annual International Virtual Meeting. Vol. 3 American Society of Agricultural and Biological Engineers, 2021. p. 1917-1924 (American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review