Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images
This addresses the problem of accurate object counting in distorted omnidirectional images for agricultural applications like grape monitoring, but it is incremental as it adapts existing methods to a new image type.
The paper tackles the problem of object counting in omnidirectional images, which conventional methods cannot handle due to distortion, by proposing a method using stereographic projection with distortion-adaptive techniques, resulting in a 14.7% improvement in mean absolute error and 10.5% in mean squared error for grape bunch counting.
This paper proposes the first object counting method for omnidirectional images. Because conventional object counting methods cannot handle the distortion of omnidirectional images, we propose to process them using stereographic projection, which enables conventional methods to obtain a good approximation of the density function. However, the images obtained by stereographic projection are still distorted. Hence, to manage this distortion, we propose two methods. One is a new data augmentation method designed for the stereographic projection of omnidirectional images. The other is a distortion-adaptive Gaussian kernel that generates a density map ground truth while taking into account the distortion of stereographic projection. Using the counting of grape bunches as a case study, we constructed an original grape-bunch image dataset consisting of omnidirectional images and conducted experiments to evaluate the proposed method. The results show that the proposed method performs better than a direct application of the conventional method, improving mean absolute error by 14.7% and mean squared error by 10.5%.