Variant Object Recognition Models for Cloud-Based Material Sorting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sorting facilities face challenges in efficiently identifying and sorting diverse materials due to limited data capture and lack of centralized AI, leading to performance and cost bottlenecks, as machine learning systems are designed for standalone devices and operate independently, restricting scalability and adaptability.
Innovation Solution
A cloud-based machine learning framework that enables distributed object recognition and sorting across facilities, using a centralized cloud sorting server to train and deploy models that can adapt to specific facility conditions and recognize variant objects, leveraging multiple sensors and machine learning techniques for accurate material identification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized cloud-based machine learning framework is implemented, then adaptability and recognition accuracy for variant objects improve, but device complexity and infrastructure requirements increase
Solution Approach 1:
A cloud-based server acts as an intermediary between multiple sorting facilities, centralizing the machine learning model training and management. The server receives sensor data from various facilities, trains unified machine learning models that can recognize diverse materials including variant objects, and distributes these models back to the facilities. This intermediary approach enables adaptability across facilities without requiring each facility to independently manage complex AI systems.
Solution Approach 2:
The cloud-based machine learning framework provides universal functionality across multiple sorting facilities. A single centralized system serves multiple facilities, enabling them to all benefit from the same advanced material recognition capabilities. The system can handle various material types and adapt to different facility conditions through a unified platform, reducing the need for facility-specific complex systems.
2Ease of manufacture
If standalone machine learning systems are used at each sorting facility, then system simplicity and ease of deployment are maintained, but recognition accuracy for diverse and variant objects deteriorates
Solution Approach 1:
The cloud-based system performs preliminary actions by training comprehensive machine learning models in advance using aggregated data from multiple facilities. These pre-trained models are then distributed to individual facilities, which can deploy them with minimal configuration. This preliminary training phase enables high recognition accuracy while keeping the actual deployment at each facility simple and straightforward.
Solution Approach 2:
The system merges data from multiple sorting facilities into a centralized cloud platform for unified model training. By combining datasets from various facilities that process different materials, the machine learning models learn from a broader variety of examples, improving recognition accuracy for diverse and variant objects. Each facility then uses this collectively trained model, benefiting from the merged data without needing to independently aggregate large datasets.
3Ease of operation
If facilities operate independently without centralized AI, then operational autonomy and simplicity are maintained, but sorting accuracy and purity rates for diverse materials deteriorate
Solution Approach 1:
The cloud-based framework implements feedback loops where sensor data and sorting outcomes from various facilities are continuously fed back to the centralized system. The machine learning models are retrained and refined using this feedback, improving their accuracy over time. The updated models are then redistributed to facilities, which continue to operate autonomously but with progressively improved sorting capabilities driven by collective learning from all facilities.
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.