Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation
  with Efficient Image Annotation
Samuel O. Folorunsho William R. Norris
Spannotation is an open source user-friendly tool developed for image
annotation for semantic segmentation specifically in autonomous navigation
tasks. This study provides an evaluation of Spannotation, demonstrating its
effectiveness in generating accurate segmentation masks for various
environments like agricultural crop rows, off-road terrains and urban roads.
Unlike other popular annotation tools that requires about 40 seconds to
annotate an image for semantic segmentation in a typical navigation task,
Spannotation achieves similar result in about 6.03 seconds. The tools utility
was validated through the utilization of its generated masks to train a U-Net
model which achieved a validation accuracy of 98.27% and mean Intersection Over
Union (mIOU) of 96.66%. The accessibility, simple annotation process and
no-cost features have all contributed to the adoption of Spannotation evident
from its download count of 2098 (as of February 25, 2024) since its launch.
Future enhancements of Spannotation aim to broaden its application to complex
navigation scenarios and incorporate additional automation functionalities.
Given its increasing popularity and promising potential, Spannotation stands as
a valuable resource in autonomous navigation and semantic segmentation. For
detailed information and access to Spannotation, readers are encouraged to
visit the project's GitHub repository at
https://github.com/sof-danny/spannotation