Optical Weight Estimation via Pixel Analysis
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Solution Overview
Problem
Existing product inspection systems lack efficient methods for automatically measuring sample weight via optical inspection, particularly in applications like almond inspection, where accurate weight measurement is crucial for quality control.
Innovation Solution
The method involves collecting measurement data using an adaptable inspection unit, determining the volume or area of the sample based on the data, and calculating the sample's weight. This is achieved by analyzing captured images and using pixel data to assess the sample's dimensions in two or three dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optical inspection is used to measure sample weight, then automation and efficiency are improved, but measurement precision may be compromised compared to traditional weighing methods
Solution Approach 1:
The patent replaces mechanical weighing systems with an optical inspection system that uses image processing and machine learning algorithms to estimate sample weight. The system captures images of samples, processes them through neural networks trained on weight-correlated visual features, and outputs weight predictions, thereby eliminating the need for physical contact with mechanical scales.
Solution Approach 2:
The system transforms the measurement parameter from direct weight detection to visual feature analysis. By training machine learning models to recognize correlations between visual characteristics (size, shape, color, texture) and weight, the system indirectly infers weight through parameter transformation rather than direct measurement.
2Measurement precision
If traditional weighing methods are used, then measurement precision is maintained, but automation and productivity are reduced
Solution Approach 1:
The system enables self-service automation where the optical inspection unit independently performs weight measurement without manual intervention. The machine learning model automatically processes images and generates weight predictions, eliminating the need for operators to manually place samples on scales and read measurements.
Solution Approach 2:
The patent introduces an intermediary computational layer between visual inspection and weight determination. Instead of direct measurement, the system uses machine learning algorithms as intermediaries that translate visual features into weight estimates, bridging the gap between optical data and weight information.
3Device complexity
If manual weight measurement is performed, then device complexity is minimized, but time consumption and productivity are increased
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on large datasets of images with known weights before deployment. This preliminary training phase enables the system to quickly infer weights during actual operation without requiring complex real-time calculations, thus reducing measurement time per sample.
Solution Approach 2:
The patent creates a digital copy or virtual model of the weighing process through machine learning. Instead of using physical weighing equipment, the system generates a computational representation that predicts weight based on visual features, effectively copying the function of mechanical scales in a digital format.
Data Source
AI summary
A method includes the steps collecting measurement data of a sample utilizing an adaptable inspection unit or while the sample is in-flight, determining a volume or area of the sample based at least in part on the measurement data, and calculating a weight of the sample based at least in part on the volume or area of the sample. The measurement data includes a captured image that includes a plurality of pixels. The determining of the volume of the sample includes determining the number of pixels in the captured image that display a portion of the sample, or determining the maximum number of consecutive pixels that display a portion of the sample in two or three dimensions.