Using deep cnn architectures and tensorflow for bee identification on images

Abstract

Problem statement. Bees are important pollinators for various plant species and are essential in maintaining our planet's biodiversity. With the decline in the number of bees worldwide, monitoring the bee hive state has become increasingly important. Pur-pose. Individual bee identification is an important task that, if performed well, will help to track the bee hive population and its health state in general. Related work. With the development of deep learning and computer vision technologies, several studies were done on their application in detecting insects like wasps, spiders, or fruit flies. Materials and methods. Photographs of bee hive frames with bees on them are being used as a da-ta source for model training. These photographs were manually annotated using Remo annotation software. This study uses five widely used deep learning architectures (Fast-er R-CNN ResNet152, CenterNet ResNet50, SSD ResNet50, Faster R-CNN Inception ResNet V2, SSD MobileNet V2) for the purpose of bee detection on test images. These models were trained on the same dataset and evaluated on the same bee image set. Re-sults and discussion. According to the confusion matrix, SSD MobileNet V2 architecture showed the best detection performance with 80% detected bees on test files, but it also had the falsest negative entries. On the other hand, Faster R-CNN ResNet152 model showed the best results in accuracy and evaluation speed, but successfully identified on-ly 61% of the bees. Conclusions. The experiment showed that state-of-the-art CNN ar-chitecture SSD MobileNet V2 is a better performer with detecting 80% and 95% accura-cy on bee images test set.