Accurate Food Sizing: A Case Study in Depth Estimation Implementation

Based on Patent Research | CN-115778188-A (2023)

Accurately determining food material size before cooking is vital for consistent product quality in food manufacturing. Inaccurate size detection often prevents optimal cooking parameter adjustments, risking undercooked or overcooked products. Depth estimation, a computer vision technique that calculates object distances from multiple images, offers a solution. This approach enables precise food sizing, leading to optimized cooking processes and improved consistency.

SOLUTION OVERVIEW

Automating Manual Sizing with AI Depth

In food manufacturing, ensuring consistent product quality hinges on accurate ingredient sizing. Depth estimation, a powerful computer vision technique, directly addresses this need. It operates by capturing multiple images of food items from different viewpoints, similar to how human eyes perceive depth (binocular vision). This data allows the system to precisely calculate the distance to objects, thereby accurately determining their exact physical dimensions. This precise sizing information then becomes a critical input for optimizing cooking parameters.

This capability enables significant operational improvements, allowing for automated adjustments in production lines. Integrating depth estimation into existing processing equipment supports dynamic adaptation, ensuring each food item, like potatoes destined for frying, receives optimal thermal treatment based on its exact size, preventing undercooking or overcooking. The technology empowers food manufacturers with enhanced precision and control, leading to superior product consistency and streamlined workflows across the entire cooking process.

HOW IT WORKS

Image Processing for Depth Estimation

Capturing Food Item Images

Specialized cameras capture multiple images of food items from different angles as they move along the production line. This process mimics human binocular vision, providing the necessary visual data for detailed analysis. The system collects a rich dataset of raw visual information for each product.

Analyzing Visual Data for Depth

The captured images are then processed to identify corresponding points across different viewpoints. Utilizing binocular vision principles, the system precisely calculates the distance of various parts of the food item from the cameras. This results in a comprehensive depth map of each individual product.

Determining Precise Food Size

Based on the detailed depth map, the system accurately computes the exact physical dimensions of each food item, such as length, width, and height. This critical step translates raw depth information into quantifiable measurements. The output is highly accurate size data for every product.

Optimizing Cooking Process Settings

The precise size information is then fed into the automated cooking equipment. This allows for dynamic and immediate adjustments to cooking parameters, including temperature and duration, ensuring optimal thermal treatment for each item. Consequently, product consistency is significantly improved, preventing undercooking or overcooking.

Potential Benefits

Improved Product Consistency

By precisely measuring food item dimensions, depth estimation ensures optimal cooking parameters are applied, preventing undercooked or overcooked products and guaranteeing uniform quality across batches.

Optimized Cooking Efficiency

Accurate sizing enables dynamic adjustments to cooking processes, ensuring each food item receives ideal thermal treatment. This leads to efficient energy use and faster production cycles.

Reduced Material Waste

Minimizing product inconsistencies directly reduces rejected batches and rework. This leads to significant savings in raw materials and energy, boosting overall profitability.

Enhanced Process Control

The system provides precise, real-time data on food item dimensions, empowering manufacturers to make informed adjustments and maintain tight control over the entire cooking workflow.

Implementation

1 Install Vision Hardware. Mount specialized cameras along the production line to capture food items from multiple angles.

2 Calibrate & Configure System. Calibrate the camera setup and configure the depth estimation software for accurate food item sizing.

3 Integrate with Automation. Connect the precise size data output directly to the automated cooking equipment controls for dynamic adjustments.

4 Optimize Process Settings. Define rules for cooking parameter adjustments based on detected food dimensions to ensure consistent product quality.

Source: Analysis based on Patent CN-115778188-A "Food material size detection method and cooking equipment" (Filed: March 2023).