A Density Map Estimation Model with DropBlock Regularization for Clustered-Fruit Counting

Abstract

Modern agricultural robots like drones have been studied in automatic yield estimation in recent years. Fruit counting is a fundamental task in the automatic yield estimation, on which significant progress has been achieved by detection-based methods and segmentation-regression-based methods. However, for clustered-fruit counting, the existing methods lack advantages on the localization of small and occluded fruits or discrete number regression. In addition, it is observed that existing deep neural network based counting methods have high variances on fruit density map estimation. Aiming at solving these two problems and decreasing the regression variance, in this paper, we propose a density-map-estimation model with DropBlock regularization. For evaluating the proposed model, we propose a new Clustered-Fruit dataset. Extensive experiments show that the proposed model is effective and outperforms the state-of-the-art counting methods on the Clustered-Fruit dataset. Our dataset is available at Clustered-Fruit.