Photo-matching Orphaned Pallets via Visual Search
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Solution Overview
Problem
The Less-than-Truckload (LTL) industry faces inefficiencies in identifying and processing orphaned pallets/handling units without identifiers, leading to customer claims and transportation delays due to conventional manual methods being labor-intensive and ineffective.
Innovation Solution
A method and system utilizing photo-matching technology, including an imaging device, feature extractor model, and visual search engine to capture and identify lost asset images, generate descriptors, and perform nearest neighbor searches within an asset database to quickly and accurately identify and route orphaned assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection and identification methods are used for orphaned pallets, then labor costs and time consumption increase significantly, but the system maintains simplicity in implementation
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated imaging and image processing system. Cameras capture images of orphaned pallets, and computer vision algorithms automatically identify and match them with missing assets, eliminating the need for manual labor while significantly improving identification speed and accuracy.
Solution Approach 2:
The system creates digital copies (images) of orphaned pallets and compares them against database records of missing assets. By working with image copies rather than physical inspection, the system enables rapid parallel processing of multiple pallets simultaneously, dramatically increasing productivity without proportionally increasing system complexity.
2Loss of time
If manual identification processes are used for lost assets, then the process becomes labor-intensive and slow, but information accuracy may be maintained through human judgment
Solution Approach 1:
The imaging system operates continuously to capture images of orphaned pallets as they arrive, eliminating the intermittent nature of manual inspection. The automated pipeline processes images continuously through matching algorithms, maintaining steady throughput and reducing overall processing time without requiring large teams of workers.
Solution Approach 2:
The system performs preliminary actions by capturing and storing images of pallets at the point of loss, creating a ready-to-process queue before manual or automated identification begins. This preliminary imaging step enables subsequent rapid matching and identification, reducing the critical path time for the entire identification process.
3Measurement precision
If comprehensive image processing and database searching are implemented for asset identification, then identification accuracy improves significantly, but computational resources and system complexity increase
Solution Approach 1:
The image processing system segments the identification task into distinct stages: image capture, feature extraction, database querying, and matching. By dividing the comprehensive analysis into manageable segments, the system achieves high identification accuracy through multiple processing steps while controlling computational energy consumption at each stage rather than requiring overwhelming resources all at once.
Data Source
AI summary
Systems and methods for lost asset management using photo-matching are disclosed herein. An example method includes capturing a lost asset image corresponding to a lost asset, and generating, by a feature extractor model, a lost asset descriptor that represents features of the lost asset image. The example method also includes storing the lost asset descriptor and the lost asset image in an asset database that includes known asset descriptors, and performing, by a visual search engine, a nearest neighbor search within the asset database to determine a respective metric distance between the lost asset descriptor and the known asset descriptors. The example method also includes determining, by the visual search engine, a ranked list of known assets corresponding to the lost asset, and displaying, at a user interface, the ranked list of known assets for viewing by a user.