AI-powered damage detection on rubber mats: Learn how we created our AI model

In today’s rapidly evolving manufacturing landscape, maintaining product quality is more critical than ever. At Data Spree, we’ve developed an AI-powered solution for detecting damage on rubber mats, providing a new approach to real-time quality control.

Rubber mats are essential in a variety of industries, from automotive to manufacturing, due to their durability, anti-slip properties, and shock absorption. However, consistent quality control is necessary to prevent deterioration and maintain their effectiveness in high-traffic environments. Our advanced AI model ensures early detection of wear and tear, preventing costly product failures.

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Steps to Build an Efficient AI Model for Rubber Mat Damage Detection

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1. Data Collection for AI Training

We started by curating a dataset of 279 high-resolution images of rubber mats. These were categorized into 218 training images and 64 testing images to train our AI model.

Using advanced imaging techniques like floodlights and bar lights, we ensured that even the smallest defects were captured.

2. Upload, Annotation, and Data Analysis

After collecting the images, we uploaded them to our AI platform, where we manually annotated damages. This was a crucial step to help the AI differentiate between pristine and damaged areas. Heatmaps were generated to visualize where the AI focused during analysis, helping to refine the model’s accuracy before training.

3. AI model training
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Several AI models were tested to determine the most effective one for detecting and classifying damage on rubber mats.

We adjusted lighting conditions and image quality to enhance the model’s predictive capabilities.

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AI Model Performance and Results

The effectiveness of our AI model was measured using key performance metrics:

• F1-Score: Our model achieved a near-perfect F1-Score, demonstrating its high accuracy in predicting both pristine and damaged mats.
• Recall: The high recall rate confirmed that our model identified all relevant cases of damage.
• Accuracy: The model consistently made correct predictions, with an accuracy score close to 1.0.
• Loss: A continuous decrease in loss during training indicated the model's steady improvement.

Our AI solution achieved 100% accuracy in real-time detection of damage, with an incredibly fast detection time of 20-30 milliseconds. This makes our model highly efficient for use in automated quality control systems in manufacturing environments.

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Interested in AI-powered Quality Control?

Our AI system can significantly enhance quality control in your manufacturing process by providing real-time damage detection. Contact us today to learn how our AI can improve your production line.

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Learn more about our quality control solution.

Find out more about our AI platform.

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