RI-DC: ROTATION-INVARIANT DETECTION AND CLASSIFICATION FOR WHEAT HEAD DETECTION

We propose a novel automatic detection method of wheat heads from images taken from above wheat fields. The automatic detection of heads is useful for predicting yields. For this purpose, deep learning based methods has proved to be effective recently, but they are not robust against the variations of head directions. To tackle this problem, we utilize a two-step approach which first carries out object detection for augmented test images rotated in many directions, and then classifies the detected objects by using a classifier trained with many rotated images. It was evaluated by using GWHD dataset and proved to be effective.