AI Trailer And Seal Identification for Faster Gate Verification
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Solution Overview
Problem
The existing systems for verifying the integrity of transportation containers are time-consuming and inefficient, particularly in identifying and validating trailer and seal information, which leads to truck backups and safety concerns due to non-standardized locations and variations in numbering and seal designs.
Innovation Solution
An AI system that uses local and global AI networks with sensor data from cameras, including PTZ capabilities and potentially drones, to identify and verify trailer and seal information by analyzing images and adjusting camera positions to meet confidence criteria, leveraging a global database for accurate identification across various locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of trailer and seal information is performed at the point of arrival, then identification accuracy can be maintained, but verification time increases significantly causing truck backups
Solution Approach 1:
The patent replaces manual mechanical verification processes with automated optical sensor systems and AI-based image recognition algorithms. Sensors capture images of trailers and seals, and AI systems automatically extract and verify identification information, eliminating the need for manual inspection while maintaining high accuracy and reducing verification time to seconds.
Solution Approach 2:
The system performs preliminary capture and preprocessing of identification information before actual verification is needed. Images are captured and pre-processed in advance, allowing the AI system to quickly retrieve and validate information when trucks arrive, thereby reducing on-site verification time and preventing backups.
2Extent of automation
If standardized locations and formats for seals and identifiers are implemented, then automation becomes easier, but adaptability to existing diverse configurations is reduced
Solution Approach 1:
The patent employs dynamic and adaptive algorithms that can adjust to various seal and identifier configurations without requiring standardization. The AI system learns and adapts to different locations, formats, sizes, and styles of seals and identifiers, enabling automation across diverse existing configurations while maintaining high recognition accuracy.
Solution Approach 2:
The system changes its detection parameters dynamically based on the specific configuration encountered. Rather than requiring fixed standardized positions, the AI system adjusts its search parameters, recognition thresholds, and validation criteria to match the actual seal and identifier configurations present on each trailer, thereby supporting both automation and configuration diversity.
3Reliability
If trucks are verified in sequence at the receiving site, then thorough inspection can be performed, but truck wait time and fuel consumption increase
Solution Approach 1:
The patent enables continuous parallel verification of multiple trucks simultaneously through automated sensor systems and AI processing. Instead of sequential inspection, multiple trucks can be verified at the same time, maintaining thorough inspection quality while eliminating wait times and reducing fuel consumption from idle trucks in queues.
Solution Approach 2:
The system rapidly processes verification through automated optical recognition and AI-based validation, skipping the time-consuming manual inspection steps. This allows trucks to be verified quickly as they arrive, preventing queues from forming and reducing the time trucks spend idle in line, thereby conserving fuel and maintaining inspection reliability.
4Measurement precision
If multiple attempts are made to identify seals and identifiers with varying configurations, then identification accuracy improves, but processing time increases
Solution Approach 1:
The patent incorporates feedback mechanisms where the AI system continuously refines its identification based on initial detection results. If a seal or identifier is not clearly recognized, the system adjusts its parameters and makes targeted additional attempts rather than exhaustive retries, improving accuracy while minimizing processing time through intelligent feedback-driven optimization.
Data Source
AI summary
A system for identifying an aspect of interest on a vehicle that includes a local AI system that can analyze sensor data from an on-site sensor to make an attempt to identify the aspect of interest according to first criterion. The aspect of interest can be information printed on the vehicle and/or on a seal of the vehicle. If the local AI system is unable to identify and validate the information on the first effort, it can consult with a central/global AI system that can leverage its own database and other local systems at other locations for subsequent attempts at identifying and validating the aspects of interest.