Location of Fruits by Counting: A Point-to-Point Approach

Abstract

The emergence of deep learning-based methods for harvesting and yield estimates, including object detection or image segmentation-based methods, has notably improved performance but has also resulted in large annotation workloads. Considering the difficulty of such annotation, a method for locating fruit is developed in this study using only center-point labeling information. To address point labeling, the weighted Hausdorff distance is chosen as the loss function of the corresponding network, while deep layer aggregation (DLA) is used to contend with the variability in the visible area of the fruit. The performance of our method in terms of both detection and position is not inferior to the method based on Mask-RCNN. Experiments on a public apple dataset are provided to further demonstrate the performance of the proposed method. Specifically, no more than two targets had positioning deviations exceeding five pixels within the field of view.