STAug: Copy-Paste Based Image Augmentation Technique Using Salient Target

Abstract

High-quality, large-capacity data are essential for training a deep learning vision model. However, to construct crop image data, absolute growth time is required for crop growth. In addition, it is characterized by unbalanced data, with fewer abnormal data than normal data. Therefore, building high-quality, large-scale datasets is challenging. Many studies have used data augmentation of plant images to solve this problem. However, plants require data augmentation that does not compromise their color, texture, or shape. This study proposes the use of salient target augmentation (STAug) as a data augmentation technique to protect the colors and shapes of plant images. The proposed method pastes one image's salient target into a different image to mix the two images. It uses a salient object detection model to generate a salient object mask of the plant. Using the generated mask, a salient target was identified and cropped in a plant image, and the cropped image data were pasted to different background data for augmentation. Concat mask, a combination of each image's salient object mask, was designed to create the label of the generated image. It is possible to create a rigid classification model by augmenting the data without damaging the plant features. To verify the performance of the proposed STAug, we compared its performance with that of other data-augmentation policies. When STAug and other augmentation techniques were applied in combination, an accuracy of 0.9733 was achieved. We demonstrated a better classification performance than when it was not applied.