Variant Object Recognition Models for Adaptive Material Sorting
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Solution Overview
Problem
Current sorting facilities face challenges in efficiently identifying and sorting diverse materials due to limited data capture and lack of centralized artificial intelligence, leading to performance and cost bottlenecks, and inefficiencies in material recognition and handling.
Innovation Solution
A cloud and facility-based machine learning system that enables distributed object recognition and sorting across multiple facilities, using a cloud sorting server to train and deploy machine learning models that can adapt to specific facility conditions and recognize various material types, including variants, through a combination of visual and non-visual sensors, and dynamic updating of object trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized machine learning system is implemented across multiple sorting facilities, then material recognition accuracy and sorting efficiency are improved, but system complexity and initial costs increase
Solution Approach 1:
The system is divided into independent modular units including image capture devices, compute nodes with machine learning models, and sorting devices at each facility. These modules can be independently configured and upgraded, reducing overall system complexity while maintaining high recognition accuracy through standardized interfaces and protocols.
Solution Approach 2:
A centralized machine learning server provides universal model training and distribution capabilities that serve multiple sorting facilities. The same core machine learning infrastructure can be adapted to different material types and sorting requirements across facilities, reducing redundancy and simplifying system deployment.
2Adaptability or versatility
If diverse material types and variants are processed, then sorting versatility is improved, but identification accuracy and sorting efficiency deteriorate
Solution Approach 1:
The machine learning models are dynamically updated and retrained based on incoming data from multiple facilities. The system adapts to new material types and variants continuously, maintaining high identification accuracy even as sorting versatility expands to cover diverse material streams.
Solution Approach 2:
The system implements feedback loops where sorting results and identification outcomes from various facilities are fed back into the centralized training process. This continuous feedback enables the machine learning models to learn from real-world performance and improve accuracy across all material types and variants.
3Productivity
If facility-specific data is used for training, then local sorting performance is improved, but overall system learning capability and adaptability worsen
Solution Approach 1:
The system merges data from multiple facilities into a centralized training process, combining local facility-specific datasets with broader system-wide data. This aggregated training approach improves local sorting performance while simultaneously enhancing overall system adaptability through shared learning across all facilities.
Solution Approach 2:
The system adds a temporal dimension to data utilization by continuously collecting and retraining on accumulating data over time. This multi-temporal training approach allows the system to maintain optimized local performance while developing broader adaptability through long-term system-wide learning patterns.
Data Source
AI summary
Using machine learning to recognize variant objects is disclosed, including: identifying an object as a variant of an object type by inputting sensed data associated with the object into a modified machine learning model corresponding to the variant of the object type, wherein the modified machine learning model corresponding to the variant of the object type is generated using a machine learning model corresponding to the object type; and generating a control signal to provide to a sorting device that is configured to perform a sorting operation on the object, wherein the sorting operation on the object is determined based at least in part on the variant of the object type associated with the object.