Storage Cabinet Marker Detection for Low-Bandwidth Article Tracking
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Solution Overview
Problem
Existing methods for detecting article store or retrieve operations in storage-retrieval cabinets rely on deep learning networks, which require significant computational resources and high bandwidth, leading to inefficiencies and high deployment costs.
Innovation Solution
A system utilizing markers on the entrance-exit edges of storage-retrieval cabinets, combined with image acquisition units and detection units, to detect article operations based on marker information, reducing the need for extensive computational resources and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning networks are used to detect article store or retrieve operations, then detection accuracy can be improved, but computational resource consumption and bandwidth requirements increase significantly
Solution Approach 1:
The patent extracts the essential detection task from complex deep learning networks and implements it through simple marker detection algorithms. By placing markers on articles and detecting their presence/absence in storage slots, the system achieves accurate detection of store/retrieve operations without requiring heavy computational resources. This extracts only the necessary detection functionality from the complex original approach.
Solution Approach 2:
The patent uses visual markers as simplified copies or representations of articles for detection purposes. Instead of analyzing complex article images directly, the system detects predetermined markers that represent article presence. This copying approach maintains detection accuracy while dramatically reducing computational requirements compared to full image analysis.
2Difficulty of detecting and measuring
If deep learning networks are deployed for article operation detection, then detection capability is enhanced, but deployment costs and system complexity increase
Solution Approach 1:
The patent extracts the core detection function from complex deep learning systems and implements it through simple computer vision algorithms that detect predetermined markers. This extraction reduces system complexity while maintaining detection capability, as the system only needs to recognize specific marker patterns rather than perform complex image analysis.
Solution Approach 2:
The patent uses simple, inexpensive markers as disposable detection elements. These markers can be easily applied to articles and removed or replaced as needed. The low cost and simplicity of these marker-based detection elements significantly reduce overall system complexity and deployment costs compared to expensive deep learning infrastructure.
3Measurement precision
If high bandwidth is allocated for image transmission in detection systems, then detection quality can be improved, but transmission costs and network requirements increase
Solution Approach 1:
The patent extracts only the essential detection information from full images by focusing on predetermined markers. Instead of transmitting and processing complete high-resolution images, the system detects specific marker features that indicate article presence. This extraction approach maintains detection quality while minimizing bandwidth consumption.
Solution Approach 2:
The patent uses compact marker representations as simplified copies of article information. These markers contain only the necessary detection data in a compressed form, eliminating the need to transmit full images. This copying mechanism achieves accurate detection with minimal bandwidth requirements.
Data Source
AI summary
Provided is a system for detecting article store or retrieve operations. The system for detecting store or retrieve operations includes: an article storage-retrieval cabinet, an image acquiring unit, and a storage or retrieval detecting unit, wherein an edge of an entrance-exit of the article storage-retrieval cabinet is provided with a marker, and the image acquiring unit is configured to acquire an image of the entrance-exit of the article storage-retrieval cabinet; and the storage or retrieval detecting unit is connected to the image acquiring unit, and is configured to detect an article store or retrieve operation based on marker information in the image acquired by the image acquiring unit.