Multi-Point Parcel Damage Detection With Machine Learning
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Solution Overview
Problem
Existing technologies for identifying and assessing parcel damage are inefficient, lacking automated detection, diagnosis, cost analysis, and machine learning capabilities, making it difficult to determine damage occurrence and mitigate it effectively.
Innovation Solution
A system utilizing digital image capture and machine learning models to analyze parcel damage across multiple interaction points in a transit network, enabling automated damage detection, diagnosis, and mitigation through programmatically generated instructions to adjust conditions or devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual input methods are used for damage identification, then device complexity is reduced, but productivity and measurement precision deteriorate due to inefficiency and lack of automation
Solution Approach 1:
The patent replaces manual mechanical input methods with an automated machine learning-based image analysis system. Digital images captured by cameras are processed through trained models that automatically detect and classify parcel damage, eliminating the need for manual inspection while significantly improving identification efficiency and precision.
Solution Approach 2:
The system enables self-service damage identification where the machine learning model autonomously analyzes images, generates damage assessments, and provides recommendations without human intervention. The automated system serves itself by continuously processing images and improving through machine learning, maintaining high productivity without proportionally increasing operational complexity.
2Measurement precision
If automated machine learning analysis is implemented, then measurement precision and productivity improve, but device complexity increases due to multiple image processing components
Solution Approach 1:
The patent implements a universal machine learning platform that handles multiple damage detection tasks through a single integrated system. The same core infrastructure processes images from various interaction points, performs different types of damage analysis, and generates comprehensive assessments, thereby improving measurement precision across multiple functions without proportionally increasing device complexity.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models with extensive damage data before deployment. This preliminary training phase establishes accurate detection capabilities that can then be applied consistently across all subsequent image analyses, achieving high measurement precision without requiring complex real-time processing adjustments.
3Reliability
If multiple digital images from various interaction points are captured and analyzed, then measurement precision and reliability improve, but loss of time increases due to extensive image processing
Solution Approach 1:
The patent segments the image analysis process into distinct stages: initial rapid screening to identify potentially damaged parcels, followed by detailed analysis only for flagged items. This segmentation allows the system to process multiple images from various interaction points reliably while minimizing total processing time by avoiding exhaustive analysis of all images.
Solution Approach 2:
The system maintains continuous useful action by processing images in real-time streams as parcels move through interaction points, rather than batch processing. This continuous analysis ensures reliable damage detection across all locations while optimizing time utilization by immediately acting on detected damage without waiting for complete data collection.
4Loss of information
If comprehensive damage analysis including cost analysis is performed, then information completeness improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent merges multiple analysis functions including damage detection, classification, and cost analysis into a single integrated machine learning pipeline. By combining these functions that share common image input and processing infrastructure, the system achieves comprehensive information output without proportionally increasing device complexity, as the same computational resources serve multiple analytical purposes.
Data Source
AI summary
A first parcel digital image associated with a first interaction point is received. The first parcel digital image may be associated with a first parcel being transported to or from the first interaction point. At least a second parcel digital image associated with at least a second interaction point is further be received. The second parcel digital image may be associated with the first parcel being transported to or from the second interaction point. A first parcel damage analysis is automatically generated based at least in part on analyzing the first parcel digital image and the at least second parcel image. The damage analysis can include determining whether the first parcel is damaged above or below a threshold.