3D Food Models for Processing from Depth Estimation

Based on Patent Research | AU-2018208441-A1 (2019)

In food manufacturing, precisely controlling processes like heating is difficult. A key challenge is determining the three-dimensional characteristics of food inside preparation devices. Current methods struggle to provide accurate spatial data, leading to inconsistent processing. Depth Estimation, a computer vision task, addresses this. It uses multiple images to understand food shape. This allows for optimized cooking and portioning. Better resource use and consistent product quality are the benefits.

SOLUTION OVERVIEW

Replacing Manual with AI Analysis

For food manufacturers struggling with inconsistent processing, Depth Estimation offers a powerful solution. This technology uses multiple images to create detailed three-dimensional models of food products. By understanding the precise shape, the system enables optimized control over cooking, portioning, and other key processes. This step-by-step approach, from image capture to 3D model generation, ensures a more consistent final product.

This technology allows for automation of quality control and precise adjustments to processing parameters. Imagine a chef needing to know the precise shape of a dough to bake it evenly; Depth Estimation provides that 3D information. This leads to better resource use and consistent product quality in areas such as baking or meat processing. Ultimately, this provides a significant operational improvement, optimizing resource allocation and enhancing decision-making throughout the food manufacturing process.

HOW IT WORKS

Getting Depth from Food Images

Capturing Food Product Images

Capturing multiple images of the food product is the first step. Cameras are strategically positioned to provide different perspectives of the food item inside the processing equipment. These images serve as the primary input for the subsequent stages of depth estimation.

Analyzing Images for Correspondences

Analyzing images for key features and corresponding points is the next process. The system identifies common features across the captured images, such as edges or textures. These matching points are crucial for calculating the depth and spatial relationships within the scene.

Calculating Depth and Generating 3D Model

Calculating Depth and Generating 3D Model is the core of the process. Using the identified correspondences, the system applies algorithms to estimate the depth of each point in the images. This depth information is then used to construct a detailed three-dimensional model of the food product.

Providing 3D Data for Process Optimization

Providing 3D Data for Process Optimization is the final stage. The resulting 3D model provides precise spatial data, enabling informed adjustments to cooking times, portion sizes, and other parameters. This leads to improved resource use and consistent product quality in food manufacturing processes.

Potential Benefits

Improved Product Consistency

Depth Estimation provides precise spatial data, leading to more consistent heating and cooking processes. This ensures that food products meet quality standards every time, reducing waste from inconsistent batches.

Reduced Resource Waste

By optimizing cooking and portioning, Depth Estimation minimizes wasted resources, such as energy and raw materials. This leads to significant cost savings in the long run for food manufacturers.

Automated Quality Control

The 3D models generated by Depth Estimation enable automated quality control, reducing the need for manual inspection. This allows for quicker identification of defects and faster corrective actions.

Enhanced Operational Insights

Depth Estimation gives food manufacturers detailed insights into their processes, which allows for better decision-making. This data-driven approach facilitates continuous improvement and optimization of operations.

Implementation

1 Camera System Setup. Install and calibrate cameras. Ensure proper lighting and stable mounting for accurate image capture.

2 Image Acquisition Protocol. Establish a protocol for capturing food images. Capture images from multiple angles, ensuring comprehensive coverage.

3 Software Configuration. Configure the depth estimation software. Set parameters for feature detection and 3D model generation.

4 3D Model Generation. Generate 3D models from captured images. Verify accuracy and refine parameters as needed.

5 System Integration. Integrate 3D data into process control systems. Adjust cooking times and portion sizes based on model data.

Source: Analysis based on Patent AU-2018208441-A1 "Food preparation entity" (Filed: June 2019).