Object Recognition System Using RFID Verification for Delivery Accuracy
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Solution Overview
Problem
Current food delivery and logistics systems rely heavily on human intervention, leading to errors such as wrong addresses, package damage, and lack of security, increasing operational costs and compromising food safety due to the absence of effective signature checks and security mechanisms.
Innovation Solution
An object recognition system utilizing image data and RFID technology to perform deep learning-based object recognition, which includes model training, verification, and augmented reality (AR) data generation to improve delivery accuracy and security through AI-driven package identification and smart lock mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning model training with image data and RFID verification is implemented, then object recognition accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs model training in advance using historical image data and RFID verification results. The pre-trained deep learning model is stored and can be directly applied to new objects without requiring real-time training, thus improving recognition accuracy while avoiding the complexity and time consumption of real-time model training.
Solution Approach 2:
The patent introduces an RFID tag as an intermediary verification mechanism. The RFID data serves as ground truth to verify the correctness of image-based classification, enabling accurate model training without requiring complex manual annotation processes. This intermediary simplifies the data verification step while improving overall system accuracy.
2Reliability
If manual signature verification is required for each delivery, then delivery security is improved, but delivery time and operational complexity increase
Solution Approach 1:
The system replaces the mechanical signature verification process with an automated object recognition system based on deep learning. The AI model automatically identifies and verifies delivery objects using image data and RFID information, eliminating the need for manual signature collection while maintaining delivery security and reducing delivery time.
Solution Approach 2:
The deep learning model performs automatic object identification and verification without requiring human intervention for each delivery. The system serves itself by autonomously recognizing objects, verifying them against the delivery list, and confirming delivery completion, thus reducing both delivery time and operational complexity while maintaining security.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances delivery precision and efficiency by reducing human error, ensuring correct package delivery, and maintaining food safety through AI-driven object recognition and smart lock technology, thereby reducing return rates and operational costs.
Implementation Method 1
an RFID device, and a deep learning processing unit configured to perform object recognition to at least one package according to the image data, the RFID data
Data Source
AI summary
An object recognition system is disclosed. The object recognition system includes an input device configured to obtain image data and a Radio Frequency Identification (RFID) data of an object, a processing device connected to the input device, and configured to perform a model training procedure, wherein the model training procedure includes capturing an object feature according to the image data, generating a classification data corresponding to the object according to the object feature, verifying a correctness of the classification data according to the RFID data, and generating a deep learning model according to the verified classification data, to regenerate the classification data, and an output device connected to the processing device, and configured to generate an object recognition data corresponding to the object according to the classification data generated by the deep learning model and the RFID data.