CNN-Based Pizza Quality Scoring and Component Segmentation
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Solution Overview
Problem
Pizza manufacturers face challenges in maintaining quality and efficiency due to manual monitoring in pizza production processes, leading to variations in quality and increased human labor costs.
Innovation Solution
A computer system utilizing convolutional neural networks (CNNs) is implemented to analyze video streams from cameras, automatically identifying and scoring pizzas by selecting the best frames, localizing pizza portions, determining pizza types, and segmenting components for accurate scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring is used in pizza production, then human labor can perform quality checks, but quality variation increases and labor costs rise
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer vision system using convolutional neural networks. The system captures images of pizzas during production and automatically analyzes quality attributes such as ingredient distribution, cooking uniformity, and portion size, eliminating human labor while maintaining consistent quality assessment
Solution Approach 2:
The pizza production line integrates self-inspection capabilities through the automated imaging system. The system autonomously captures, processes, and evaluates pizza quality without requiring external human intervention, enabling continuous monitoring and immediate feedback for quality control
2Productivity
If automated systems are implemented, then productivity and consistency improve, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional convolutional neural network system that performs multiple quality assessment tasks including ingredient detection, portion size measurement, cooking uniformity evaluation, and defect identification within a single integrated platform, reducing the need for multiple separate systems
Solution Approach 2:
The system performs preliminary quality assessment during the production process itself rather than as a separate post-production step. Images are captured and analyzed in real-time, allowing for immediate detection and correction of quality issues before pizzas leave the production line
Data Source
AI summary
In some embodiments, the present invention provides for an exemplary inventive convolutional neural network-based and computer-implemented method for identifying and evaluating pizza, including: collecting video input representative of the food and received from at least one camera, applying a first CNN to select, from the video input, a set of best pizza containing video frames of a particular pizza from the plurality of pizza containing video frames; applying the first CNN to identify a best pizza containing image from the set, to localize at least one pizza portion of the particular pizza in the identified best pizza containing image, and to determine a type of the pizza of the particular pizza from the identified best pizza containing image; applying a second CNN to determine a map of pizza components of the particular pizza by automatically performing pizza image segmentation and to automatically score the particular pizza based on the determined map of pizza components.