Ensure Cooking Consistency with Image Segmentation

Based on Patent Research | WO-2023144259-A1 (2023)

In food manufacturing, accurately determining cooking phases is challenging. Inconsistent phase detection leads to suboptimal results. Current methods often fail to adapt to the food's changing form. Image segmentation, a computer vision technique, can solve this. It identifies and tracks the food's geometric shapes as it cooks. This allows for precise detection of cooking phases. The result is improved food quality, consistency, and a better production process.

SOLUTION OVERVIEW

Replacing Manual with AI Detection

For food manufacturing professionals, image segmentation technology offers a solution to the challenges of accurately determining cooking phases. This computer vision technique analyzes visual data to identify and track the geometric shapes of food as it cooks. By segmenting the image, the system precisely detects cooking phases. This process allows for real-time monitoring and assessment of food products during production.

This technology brings practical advantages, such as automation and seamless integration with existing quality control systems. For example, it is similar to tracking the different stages of bread rising in an oven to bake it perfectly. This ensures optimal cooking and prevents under or over-processing. Ultimately, image segmentation provides significant operational improvements and resource optimization, leading to enhanced decision-making and consistent, high-quality food products.

HOW IT WORKS

Images to Cooking Phase Conversion

Capturing Food Product Images

Capturing images of food products initiates the process. High-resolution cameras acquire detailed images of food items as they progress through the cooking process. These images serve as the primary input for subsequent analysis and phase detection.

Analyzing Images for Key Features

Analyzing image data to identify key features is crucial. The system processes the captured images using image segmentation techniques, precisely delineating different regions and geometric shapes within the food. This analysis highlights changes in shape and texture, which indicate different cooking phases.

Detecting Cooking Phases

Detecting cooking phases based on segmented images allows for real-time monitoring. By tracking the geometric changes identified through image segmentation, the system determines the current cooking phase of the food product. This real-time phase detection enables timely adjustments to the cooking process, ensuring optimal results.

Optimizing the Cooking Process

Optimizing the cooking process based on phase detection is the final step. The system uses the detected cooking phase to automatically adjust cooking parameters, such as temperature or cooking time. This optimization ensures consistent food quality and prevents under or over-processing, leading to improved production efficiency and reduced waste in food manufacturing.

Potential Benefits

Improved Product Consistency

Image segmentation provides consistent monitoring of food products during cooking. This leads to more uniform results, reducing variability in taste and texture across batches.

Reduced Waste and Energy Consumption

By accurately detecting cooking phases, the system optimizes processing times. This prevents over- or under-processing, saving energy and minimizing waste of raw materials.

Enhanced Control Over Production

The AI provides real-time data on food's geometric changes during cooking. This allows for immediate adjustments, leading to better control over the production process and higher quality output.

Streamlined Quality Control

Automated image analysis reduces the need for manual inspection. This streamlines quality control and frees up personnel for other critical tasks in the food manufacturing process.

Implementation

1 Camera System Setup. Install high-resolution cameras above cooking lines. Ensure proper lighting and stable mounting for consistent image capture.

2 Image Data Acquisition. Collect images of food products during cooking. Capture various stages to build a comprehensive dataset for training.

3 Dataset Annotation. Label images with cooking phase information. Precise labeling is crucial for accurate model training and performance.

4 Model Configuration. Configure the image segmentation model. Adjust parameters to optimize performance for specific food types.

5 System Integration. Integrate the system with existing control systems. Enable automated adjustments to cooking parameters based on phase detection.

6 Ongoing Monitoring. Monitor system performance and retrain as needed. Continuously improve accuracy with new data and feedback.

Source: Analysis based on Patent WO-2023144259-A1 "Cooking process implementation" (Filed: August 2023).