SIFT-Guided Saliency-Based Augmentation for Weed Detection in Grassland Images: Fusing Classic Computer Vision with Deep Learning

Patrick Schmidt, Ronja Güldenring, Lazaros Nalpantidis

Abstract: Weed detection is a challenging case within object detection as the weed targets do not generally strike out from the background in terms of color. This paper investigates how the density of structural features can be used to assist the training process of a Deep-Learning-based object detector. SIFT keypoint density is used to create overlay masks to augment images, emphasizing low-density areas—typically corresponding to weed plants. Our method is shown to improve detection \(mAP_{.5:.05:.95}\) on the YOLOR-CSP detector by up to 0.0215.