Authors:
Yassine Kriouile
1
;
2
;
Corinne Ancourt
2
;
Katarzyna Wegrzyn-Wolska
1
;
2
and
Lamine Bougueroua
1
Affiliations:
1
EFREI Paris, AliansTIC, 30/32 Avenue de la République, 94800 Villejuif, France
;
2
Mines ParisTech, PSL University, Centre de Recherche en Informatique, 35 rue Saint Honoré, 77300 Fontainbleau, France
Keyword(s):
Bees, Object Detection, Faster RCNN, RPN, High Object Density, Corners.
Abstract:
Detecting bees in beekeeping is an important task to help beekeepers in their work, such as counting bees, and monitoring their health status. Deep learning techniques could be used to perform this automatic detection. For instance Faster RCNN is a neural network for object detection that is suitable for this kind of tasks. But its accuracy is degraded when it comes to images of bee frames due to the high density of objects. In this paper, we propose to extend the RPN sub-neural network of Faster RCNN to improve detection recall. In addition to detect bees from centers, four branches are added to detect bees from their corners. We constructed a dataset of images and annotated it. We compared this approach to the standard Faster RCNN. It improves the detection accuracy. Code is available at https://github.com/yassine-kr/RPNCorner.