Computer Vision Segmentation Training for Crowded Inventory Picking
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Solution Overview
Problem
Modern inventory systems face inefficiencies in resource utilization, leading to low throughput, long response times, and increased backlogs due to challenges in handling diverse inventory requests, particularly in environments with crowded and complex scenes where conventional machine-learning models struggle with accurate object segmentation and manipulation.
Innovation Solution
The implementation of a semi-supervised training technique using a MasQ R-CNN model that incorporates a mask quality score and heavy augmentations, allowing for improved instance segmentation and robotic manipulation by leveraging labeled, pseudo-labeled, and unlabeled data to enhance the performance of computer vision models in dense environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine-learning models are used for object segmentation in crowded inventory scenes, then the system can handle basic inventory tasks, but the segmentation accuracy deteriorates and manipulation failures increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into multiple stages: initial object detection, instance segmentation to separate overlapping objects, and quality assessment of segmentation masks. This multi-stage segmentation approach enables accurate identification and manipulation of individual items in crowded inventory scenes, directly resolving the contradiction between segmentation accuracy and manipulation reliability
Solution Approach 2:
The patent implements feedback through a quality assessment mechanism that evaluates segmentation mask quality scores and uses this feedback to filter or refine segmentation results before manipulation. This feedback loop ensures that only high-quality segmentations proceed to manipulation, thereby maintaining both segmentation accuracy and manipulation success rate
2Productivity
If more system resources are allocated to handle diverse inventory requests, then throughput can be improved, but resource utilization efficiency deteriorates due to inefficient task completion
Solution Approach 1:
The patent applies self-service through autonomous robotic systems that use computer vision to automatically identify, segment, and manipulate inventory items without human intervention. The system serves itself by making independent decisions about task prioritization and execution, improving throughput while maintaining resource efficiency through automated optimization of task completion processes
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting system operations based on real-time conditions. The quality assessment mechanism changes parameters such as segmentation threshold and manipulation priority based on item characteristics, enabling efficient resource allocation that improves throughput without wasting resources on low-priority or difficult-to-manipulate items
3Loss of time
If response time is reduced to handle inventory requests faster, then customer service improves, but segmentation accuracy deteriorates in complex scenes
Solution Approach 1:
The patent applies preliminary action by performing quality assessment and filtering of segmentation masks before the manipulation stage. This preliminary evaluation ensures that only high-quality segmentations are selected for manipulation, maintaining segmentation accuracy even under time-constrained conditions by pre-identifying reliable segmentations rather than relying on post-processing corrections
Solution Approach 2:
The patent implements partial action by applying quality assessment selectively to segmentation results based on confidence scores and scene complexity. In simple scenes, full assessment is performed, while in time-critical situations, the system uses partial assessment focused on high-priority items, balancing response time requirements with segmentation accuracy maintenance
Data Source
AI summary
Techniques for training a machine-learning model are described. In an example, a computer generates a first pseudo-label indicating a first mask associated with a first object detected by a first machine-learning model in a first training image. A transformed image of the first training image can be generated using a transformation. Based on the transformation, a second pseudo-label indicating a second mask detected in the transformed image and corresponding to the first mask can be determined. A second machine-learning model can be trained using the second pseudo-label. The trained, second machine-learning model can detect a third mask associated with a second object based on a second image.