Vison Based AI Electronic Scale

Introduction:

Smart electronic weighing scales are widely utilized in supermarkets and neighborhood fruit/vegetable shops to enhance work efficiency. These devices integrate weighing and POS (point-of-sales) software, streamlining operations in shops.

However, current smart electronic weighing scales face certain challenges:

1. The system requires manual item selection on the scale, which can be time-consuming for shoppers unfamiliar with the operation.

2. Incorrect item selection leads to pricing errors.

To address these issues and further enhance the efficiency of the weighing process, a vision-based AI Electronic Scale solution has been introduced to the market.

Additional hardware needed: AI camera.

Operational Process Flow:

1. Place the items on the scale.

2. Once the scale detects the weight, the camera activates and captures an image of the items on the scale.

3. The system utilizes item identification algorithms to identify each item.

4. The software calculates the total price by multiplying the unit price with the weight. It then either prints a label, generates a receipt, or proceeds with the payment process.

Item Identification Model Creation:

1. Load the image data of items with labeling.

2. Convert the image to grayscale.

3. Apply morphological opening to enhance features.

4. Utilize a CNN (Convolutional Neural Network) approach to extract features.

During Operation:

1. Capture the image when the weighing result stabilizes.

2. Convert the image to grayscale.

3. Perform item identification.

Discussion:

In practical scenarios, the device may not have internet connectivity, necessitating an offline identification algorithm.

Moreover, the shop may sell items do not present in the existing dataset. In such cases, a transfer learning model can be employed, allowing shoppers to load actual item images for feature extraction using a pre-trained model.

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