Automated Cooking Quality Control with Object Detection

Based on Patent Research | CN-110507203-B (2022)

Maintaining consistent food preparation presents ongoing challenges. Current cooking methods often need constant supervision. This makes consistent meal preparation difficult. Object detection, a computer vision task, can identify food items and their state. This technology enables automated cooking and reduces the need for constant monitoring. Automating the cooking process ensures consistent quality. It also helps tailor meals to individual health needs. This leads to better efficiency and personalized nutrition in food manufacturing.

SOLUTION OVERVIEW

Manual Cooking to AI Automation

Object detection offers a precise solution to the challenges of maintaining consistent food preparation in food manufacturing. This technology uses cameras to visually scan food items during cooking. AI algorithms then analyze these images, identifying the food and assessing its cooking state. This feedback enables automated adjustments to cooking parameters, ensuring uniform results across all batches and reducing variability in the final product.

This approach allows for the automation of cooking processes and integration with existing food production lines. Think of it as a self-regulating oven that adjusts temperature based on visual feedback, ensuring optimal results. Object detection minimizes waste by preventing overcooking or undercooking and supports customized meal preparation based on visual analysis of ingredients. This leads to significant operational improvements in food manufacturing, optimizing resource use and enhancing the consistency of food products.

HOW IT WORKS

Spotting Ingredients in Cooking Videos

Capturing Food Item Images

Capturing images of food items is the first step. Cameras strategically placed on the food production line visually scan the food as it's being cooked. These images provide the raw data for the AI system to analyze.

Analyzing Images with AI Algorithms

Analyzing Images with AI Algorithms involves processing the captured images using object detection models. These algorithms identify different food items present in the images, along with their cooking state, such as raw, partially cooked, or fully cooked. This analysis provides detailed information about the food's current condition.

Assessing Cooking State and Quality

Assessing Cooking State and Quality happens after the AI algorithms have analyzed the images. Based on the visual data, the system assesses the cooking state of each food item, determining if it meets the desired quality standards. This assessment is crucial for ensuring consistent results.

Adjusting Cooking Parameters Automatically

Adjusting Cooking Parameters Automatically is the final step. Based on the assessed cooking state, the system automatically adjusts cooking parameters, such as temperature and cooking time. These adjustments ensure that each batch of food is cooked to the optimal level, minimizing waste and maximizing consistency.

Potential Benefits

Consistent Food Quality Assurance

AI-powered object detection ensures consistent cooking quality across all food batches. This reduces variability and guarantees a uniform product, meeting customer expectations every time.

Reduced Food Waste and Costs

By automating cooking adjustments based on real-time visual analysis, the system minimizes waste. Overcooking or undercooking is prevented, optimizing resource utilization.

Personalized Meal Preparation Support

Object detection provides the ability to customize meals based on identified ingredients. This technology supports the production of food tailored to individual dietary requirements.

Improved Operational Efficiency

Automating cooking processes reduces the need for constant human supervision. This allows staff to focus on other critical tasks, increasing overall efficiency in food production.

Implementation

1 Camera System Setup. Install high-resolution cameras above cooking lines. Ensure proper lighting and clear field of view.

2 Image Data Acquisition. Collect images of food items during cooking. Capture variations in raw, partially cooked, and fully cooked states.

3 Dataset Annotation. Label images with food types and cooking states. Create a comprehensive dataset for model training.

4 Model Training. Configure and train object detection model. Optimize for accuracy in identifying food and its cooking level.

5 System Integration. Integrate the AI model with cooking equipment. Enable automated adjustments of temperature and cooking time.

6 Ongoing Monitoring. Monitor the system's performance continuously. Retrain the model with new data to maintain accuracy.

Source: Analysis based on Patent CN-110507203-B "Small-size integrated intelligent cooking equipment" (Filed: November 2022).