Automated Freight Damage Detection Using Machine Learning

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Solution Overview

Problem

Freight damage claims in the logistics industry are challenging to detect and classify due to the difficulty in manually logging damages, especially with high volumes of shipments, leading to inefficiencies and unorganized records, and the inability to determine when and where damages occurred.

Innovation Solution

A computer program and system utilizing a remote server with a machine learning algorithm that analyzes 3D models, sensor data, pictures, and videos to identify and classify damage, providing a percentage likelihood of damage and type, with the ability to adjust based on user feedback to improve accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual damage logging is used, then simplicity of operation is maintained, but productivity decreases and time is wasted

Engineering Contradiction:

Improvesimplicity of damage loggingVSAvoiddamage assessment efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces manual mechanical inspection and logging processes with an automated computer vision system using cameras, machine learning algorithms, and image processing to detect, assess, and classify freight damage automatically, eliminating the need for manual labor while significantly improving productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service damage assessment where the automated system independently performs inspection, detection, classification, and logging of damages without requiring human intervention, allowing the freight inspection process to serve itself

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual inspection of each item is performed, then measurement precision of damage detection is improved, but loss of time increases

Engineering Contradiction:

Improvedamage detection accuracyVSAvoidinspection time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces time-consuming manual visual inspection with automated computer vision technology using cameras and machine learning algorithms that can rapidly and accurately detect damage across large volumes of freight items simultaneously

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables continuous automated inspection throughout the freight handling process rather than intermittent manual checks, maintaining constant surveillance and detection capability to identify damage at the moment it occurs without time loss

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If extensive manual logging is performed, then measurement precision of damage records is improved, but device complexity increases

Engineering Contradiction:

Improvedamage record accuracyVSAvoidlogging system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex manual logging procedures with an automated digital system that uses computer vision, machine learning, and electronic data processing to accurately record damage information, reducing operational complexity while maintaining or improving record precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates digital copies and representations of damaged freight items through photography and 3D modeling, allowing precise damage documentation and analysis without requiring complex physical logging procedures or handling the actual damaged goods repeatedly

Inventive Principle:

Principle #26Copying

4Reliability

If frequent manual inspection is conducted, then reliability of damage detection is improved, but loss of time increases

Engineering Contradiction:

Improvedamage detection reliabilityVSAvoidinspection frequency time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements continuous automated inspection throughout the entire freight chain using mounted cameras and real-time image processing, ensuring reliable damage detection at all times without the time penalties of repeated manual inspections

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system replaces unreliable manual inspection with automated computer vision technology that provides consistent, objective, and reliable damage detection through algorithms trained to identify various damage types with high accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11727522B2Method, system, and apparatus for damage assessment and classification

Publication Date: 2023.08.15 CARGO SPECTRE

AI summary

A computer implemented service for identifying and classifying damage. The algorithm may be implemented on a device, such as a computer or mobile device, or on a remote server. The remote server may be a website or cloud-based platform. A user may access the service by sending a request to the remote server including an image, video, or live feed containing an item to be inspected. The service may identify and classify any damage found on the item. The output of the service may include the location of the damaged item, a determination of the presence of damage, a certainty level of this determination, and a heatmap indicating the areas of the image that are most likely to contain damage. The output of the service may be stored on a remote server or may be integrated into existing damage reporting systems.