Carton Damage Detection via Machine Learning Imaging
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Solution Overview
Problem
Existing supply chain infrastructures face challenges in accurately identifying and attributing damage to cartons due to handling issues within the supply chain, as manual recording methods are often inaccurate and inconsistent, making it difficult to determine the root cause of damage.
Innovation Solution
Implementing a carton damage detection system that uses camera systems and machine learning models to automatically capture images of cartons and assess damage likelihood, storing records in a database to identify damaged cartons and determine root causes of damage within the supply chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual recording methods are used to document carton damage, then users can record damage observations, but the accuracy and consistency of damage attribution deteriorates due to human error and subjective assessment
Solution Approach 1:
The patent replaces the manual mechanical recording process with an automated image capture and analysis system. Cameras mounted on equipment automatically photograph cartons at various supply chain locations, and machine learning algorithms analyze these images to objectively determine damage, eliminating human subjectivity and error in damage assessment
Solution Approach 2:
The system creates visual copies (photographs) of cartons at each location where they are handled. These image records serve as objective evidence of carton condition, allowing accurate tracking of when and where damage occurred without relying on human memory or subjective reporting
2Measurement precision
If automated image capture and machine learning analysis are implemented, then detection accuracy and productivity improve, but device complexity increases
Solution Approach 1:
The patent integrates multiple functions into a single automated system: image capture, machine learning analysis, damage determination, and record storage all occur within one system. This multi-functionality improves detection accuracy while managing complexity by consolidating operations rather than using separate manual processes for each function
Solution Approach 2:
The machine learning model automatically analyzes images and determines damage without human intervention. The system serves itself by autonomously processing images, comparing them against learned patterns of damage, and generating damage determinations, which reduces the need for complex manual analysis procedures
3Loss of information
If cameras are positioned at multiple routing locations to track cartons, then the ability to identify root cause locations improves, but the quantity of images and data to be processed increases
Solution Approach 1:
The patent divides the supply chain into discrete segments or locations, placing cameras at each segment. This segmentation allows the system to track cartons through specific stages and identify exactly which location caused damage, preventing information loss about the damage origin while managing data through structured location-based categorization
Data Source
AI summary
Methods and systems for automated detection of carton damage are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.