Production Line Image Preprocessing for Reliable Fill Level Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image-based prediction techniques for production line conformance monitoring are prone to performance deficiencies due to variations in image attributes such as orientation, lighting, and noise, leading to false negatives, particularly in identifying fill levels of pill containers.
Innovation Solution
Implementing intelligent optimization of model input data for machine learning detection models by generating cropped and transformed images based on predefined object attributes, using geometric transformations and machine learning models to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image-based prediction techniques are used for production line conformance monitoring, then the system can process images directly, but the model performance deteriorates due to variations in image attributes such as orientation, lighting, and noise leading to false negatives
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing operations (cropping, resizing, orientation correction, lighting normalization) before the machine learning model processes the images. This prepares the input data in advance to eliminate harmful variations in orientation, lighting, and noise, ensuring consistent model performance across different production line conditions
Solution Approach 2:
The patent introduces an intermediary image preprocessing module that acts as a mediator between the raw production line images and the machine learning model. This intermediary layer transforms the harmful variable image attributes into standardized formats, allowing the model to focus on detecting actual conformance issues without being affected by environmental variations
2Measurement precision
If comprehensive image processing is performed to account for all object attributes, then model accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: initial cropping to remove irrelevant background, resizing to standard dimensions, selective orientation correction based on detected object orientation, and targeted lighting normalization. This segmented approach processes only the necessary attributes for each image, maintaining high measurement precision while avoiding unnecessary computational overhead that would reduce productivity
Solution Approach 2:
The patent implements partial action by applying image processing operations selectively rather than uniformly to all images. The system detects specific attributes (such as orientation or lighting issues) and applies only the necessary corrections, avoiding excessive processing of already suitable images. This maintains high detection accuracy for fill levels while preserving processing throughput by minimizing unnecessary computational steps
Data Source
AI summary
Various embodiments of the present disclosure provide production line conformance measurement techniques using intelligent optimization of model input data for a machine learning detection model. The techniques may include generating a cropped image from a production line image based on an outer circumference associated with a production line item, generating a derivative cropped image from the cropped image based on an interior circumference associated with the production line item, generating a transformed input image from the derivative cropped image based on one or more model parameters of a machine learning detection model, and generating, using the machine learning detection model, a prediction output based on the transformed input image.