Nested object detection using mask R-CNN: application to bee and varroa detection

AbstractIn this paper, we address an essential problem related to object detection and image processing: detecting objects potentially nested in other ones. This problem exists particularly in the beekeeping sector: detecting varroa parasites on bees. Indeed, beekeepers must ensure the level of infestation of their apiaries by the varroa parasite which settles on the backs of bees. As far as we know, there is no yet a published approach to deal with nested object detection using only one neural network trained on two different datasets. We propose an approach that fills this gap. Therefore, we improve the accuracy and the efficiency of bee and varroa detection task. Our work is based on deep learning, more precisely Mask R-CNN neural network. Instead of segmenting detected objects (bees), we segment internal objects (varroas). We add a branch to Faster R-CNN to segment internal objects. We extract relevant features for internal object segmentation and suggest efficient method for training the neural network on two different datasets. Our experiments are based on a set of images of bee frames, containing annotated bees and varroa mites. Due to differences in occurrence rates, two different sets were created. After carrying out experiments, we ended up with a single neural network capable of detecting two nested objects without decreasing accuracy compared to two separate neural networks. Our approach, compared to traditional separate neural networks, improves varroa detection accuracy by 1.9%, reduces infestation level prediction error by 0.22%, and reduces execution time by 28% and model memory by 23%. In our approach, we extract Res4 (a layer of the ResNet neural network) features for varroa segmentation, which improves detection accuracy by 11% compared to standard FPN extraction. Thus, we suggest a new approach that detects nested objects more accurately than two separate network approaches.