  Object detection plays a crucial role in the field of computer vision by
autonomously identifying and locating objects of interest. The You Only Look
Once (YOLO) model is an effective single-shot detector. However, YOLO faces
challenges in cluttered or partially occluded scenes and can struggle with
small, low-contrast objects. We propose a new method that integrates spatial
transformer networks (STNs) into YOLO to improve performance. The proposed
STN-YOLO aims to enhance the model's effectiveness by focusing on important
areas of the image and improving the spatial invariance of the model before the
detection process. Our proposed method improved object detection performance
both qualitatively and quantitatively. We explore the impact of different
localization networks within the STN module as well as the robustness of the
model across different spatial transformations. We apply the STN-YOLO on
benchmark datasets for Agricultural object detection as well as a new dataset
from a state-of-the-art plant phenotyping greenhouse facility. Our code and
dataset are publicly available.
