Image Annotation Best Practices for Robotics

High-quality image annotation is essential for the performance of autonomous robotics systems, which need to operate with sub-pixel precision in dynamic environments. The process of data annotation, or data labeling, involves marking objects, obstacles, or regions of interest in images or video frames, effectively programming a robot’s visual understanding. Roboflow offers a platform to manage this data pipeline, supporting best practices for annotation, such as using manual, semi-automatic, and automatic methods depending on project needs. Manual annotation is ideal for small, critical datasets requiring high accuracy, while semi-automatic annotation with Label Assist can speed up labeling with human oversight. Automatic annotation using foundation models is fastest but requires subsequent human review, especially for safety-critical applications. Active learning is recommended for large-scale projects, focusing on difficult cases to improve models iteratively. The guide emphasizes consistent annotation practices, such as labeling every object instance, maintaining annotation consistency, and including negative examples to prevent false positives. It also covers specific annotation techniques for handling occlusions, motion blur, reflective objects, and multi-instance scenarios, as well as the importance of dataset health checks and class management. The overall goal is to create robust datasets that enable robots to safely and efficiently perform tasks in diverse, real-world conditions.