Robot Image Annotation Using Multi-Angle Semantic Segmentation
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Solution Overview
Problem
There is a need for robots to automatically identify and annotate items without manual teaching by a user, enabling them to sort and move items to designated locations efficiently.
Innovation Solution
A robot equipped with imaging devices and a controller that captures images, identifies target areas, obtains depth information, calculates the center of items, rotates the imaging devices, and captures images at different angles to enhance semantic segmentation and item identification, allowing it to automatically annotate and sort items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot captures images at multiple angles to improve item identification accuracy, then the annotation precision is improved, but the time required for capturing and processing images increases
Solution Approach 1:
The system performs preliminary actions by capturing multiple angled images and computing feature descriptors before the actual item identification task. This pre-processing creates a comprehensive visual database that enables faster and more accurate identification during operation, resolving the contradiction between thoroughness and speed.
Solution Approach 2:
The system dynamically adjusts the imaging process by rotating the imaging device to capture images at different angles (e.g., 0°, 45°, 90°, 135°). This dynamic multi-angle capture strategy ensures comprehensive item recognition while maintaining efficiency through systematic angular progression.
2Manufacturing precision
If the robot uses depth information to calculate item centers for precise imaging, then the manufacturing precision is improved, but the device complexity increases
Solution Approach 1:
The system introduces depth information as an intermediary element that bridges the imaging device and the item. By using depth data to calculate item centers and adjust imaging parameters, the system achieves precise manufacturing-level imaging without requiring complex mechanical positioning systems, thus resolving the contradiction between precision and complexity.
3Productivity
If the robot automatically annotates items without manual teaching, then the productivity is improved, but the reliability of item classification may worsen
Solution Approach 1:
The system implements feedback mechanisms where captured images are processed to extract feature descriptors, compared against known item patterns, and used to automatically annotate and classify items. This automated feedback loop enables high-speed classification while maintaining reliability through systematic comparison and recognition algorithms.
Solution Approach 2:
The system creates visual copies (feature descriptors) of items from multi-angle images and uses these copies for automated identification and classification. This copying approach enables rapid automatic annotation without manual teaching, resolving the contradiction between productivity and reliability by allowing fast comparison against stored visual patterns.
Data Source
AI summary
A robot automatically annotates items by training a semantic segmentation module. The robot includes one or more imaging devices, and a controller comprising machine readable instructions. The machine readable instructions, when executed by one or more processors, cause the controller to capture an image with the one or more imaging devices, identify a target area in the image in response to one or more points on the image designated by a user, obtain depth information for the target area, calculate a center of an item corresponding to the target area based on the depth information, rotate the imaging device based on the center, and capture an image of the item at a different viewing angle in response to rotating the view of the imaging device.