Visualizing Cooking Stages through Object Detection

Based on Patent Research | CN-115191838-A (2022)

In food manufacturing, understanding automated cooking processes is key. Currently, users lack insight into how AI controls cooking appliances. This makes it hard to trust the appliance's actions. Object detection, a computer vision task, can help by identifying key cooking stages. This AI capability displays these stages on a timeline. Users gain a clearer understanding, which builds trust in the automated process and cooking progress.

SOLUTION OVERVIEW

Manual Inspection Transformed by AI Detection

Object detection offers a solution to improve automated cooking processes. This technology identifies specific cooking stages by analyzing visual data from cameras. It then presents this information on a timeline, giving users a clear understanding of the cooking process. The system gathers initial images, processes them to detect key elements, and presents the results on a visual display, enhancing transparency in the automated process.

This technology can be integrated into existing industrial kitchen appliances. Object detection automates the monitoring of cooking stages. Think of it like a GPS for cooking, showing exactly where the process is and what's coming next. By providing clear visual feedback, object detection increases user trust and confidence in automated cooking, ultimately improving the efficiency and reliability of food manufacturing operations.

HOW IT WORKS

Analyzing Images for Food Defects

Capturing Cooking Stage Images

Capturing images of the cooking process begins with cameras strategically placed to monitor the food. These cameras record the food as it cooks, providing a visual stream of data. The images capture various cooking stages, such as browning, simmering, or boiling.

Analyzing Images for Key Indicators

Analyzing images for key indicators involves the AI system using object detection to identify specific visual cues. The system looks for features like color changes, texture variations, or the presence of certain ingredients in a particular state. This analysis determines the current cooking stage based on these identified elements.

Identifying the Specific Cooking Stage

Identifying the specific cooking stage is done by matching the detected features to pre-defined cooking stage categories. The AI compares the analyzed image data to its trained understanding of different stages, such as 'pre-heated', 'cooking', or 'ready'. This matching process assigns a label to the current state of the food.

Displaying Cooking Progress on a Timeline

Displaying cooking progress on a timeline then presents the identified stages in a clear, visual format. The timeline shows the progression of the cooking process, highlighting the current stage and any completed stages. This visual representation allows users to easily understand the AI's assessment of the cooking process.

Potential Benefits

Increased Trust in Automation

Increased Trust in Automation Visual timelines of cooking stages build confidence in automated processes, ensuring food manufacturers can rely on AI-driven systems.

Enhanced Process Monitoring

Enhanced Process Monitoring Object detection automates the monitoring of cooking stages, providing real-time insights into the progress of each batch and identifying potential issues early.

Improved Product Consistency

Improved Product Consistency By precisely tracking cooking stages, the AI ensures consistent results, reducing variability in product quality and minimizing waste.

Data-Driven Process Optimization

Data-Driven Process Optimization The AI generates data on cooking processes, allowing manufacturers to identify bottlenecks and optimize parameters for maximum efficiency and throughput.

Implementation

1 Camera System Setup. Install cameras to monitor cooking. Calibrate for optimal image quality and coverage.

2 Image Data Collection. Gather images of various cooking stages. Label data to train the object detection model.

3 Model Configuration. Configure the object detection model. Adjust parameters based on food type.

4 System Integration. Integrate AI with cooking appliance controllers. Ensure seamless data transfer.

5 Timeline Visualization. Display real-time cooking stages on a timeline. Provide clear visual feedback to users.

Source: Analysis based on Patent CN-115191838-A "Display device of cooking utensil and cooking utensil" (Filed: October 2022).