Bayesian method for bee counting with noise-labeled data.

Bee counting is an essential task for monitoring the health of bee colonies. However, it is challenging, as bees are often small and difficult to see. One approach to bee counting is detecting individuals and using that information to count. However, this approach can be unreliable if the data are noisy, meaning some labels are incorrect. This paper presents a novel approach to bee counting with noise-labeled data. The proposed method uses a density-based counter that is robust with noisy data. Experimental results show that the proposed method can achieve significantly higher accuracy than the detection-based approach for bee counting with noise-labeled data. The proposed method can improve the accuracy of bee counting in various scenes, such as different weather conditions and the density of bees. The approach could also be used to develop new tools for monitoring bee populations. Source codes of the proposed method and the dataset are publicly available on GitHub.