Detection model (neural networks)

inteligencia artificial aplicaciones en la industria

In which industries can I apply it?

Any industry that requires detecting objects on images in real time.

What are the possible applications in industry or business?

By training the data necessary for the detection model with neural networks, it is possible to carry out the following applications in the industry:

  • Quality management: For the quality management of different products, an artificial intelligence model can be developed to automatically distinguish defective products and be able to correct them efficiently.
  • Inventory management: It is possible to carry out an inventory management automatically, where the model counts all the inventory objects, avoiding human error.
  • Assembly line: Through artificial intelligence, products can be correctly located and differentiated to correlate with their movement, leading to higher production and a more efficient workforce.
  • Surveillance through CCTV: Using an object detection model, you can identify some unwanted items at a specific site; such as a weapon on a bench, a person in a restricted area, or a person without a mask.
  • Custom Object Detection: Objects come in a variety of shapes, and algorithms typically need thousands of training examples to learn how to differentiate products.
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What impact does it have on the business?

Given the great flexibility that this model can have, it is possible to generate an impact on the optimization of industrial processes such as quality management, inventory management, assembly, among others. As well as increasing the security of a specific site by implementing a model through security cameras.

What problem does the model solve?

Through the detection model with neural networks, objects can be located and classified using images or videos. It is possible to train this model with different databases, but in this specific case, it was trained with the COCO database, which is of great importance in computer vision models. Through this, it is possible to detect everything from road agents such as people, cars, bicycles, buses, to smaller objects such as cell phones, computers and televisions.

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How is this detection model different?

  • The difference it has with the usual object detection models is that the model, being directly deployed using JavaScript, allows it to be integrated into any web page.
  • The browsers have an interactive space that allows access to devices such as the webcam, which provide the images as inputs to the detection model.
  • It is easier to implement from the developer side.
  • The model can make predictions on different devices:
    • JavaScript applications in the web browser.
    • Servers within a Node.js environment.
    • Mobile browsers on Android or iOS devices.
    • Desk.


The first step is to train the model using the Common Objects in Context public dataset, making use of Google’s Tensor Processor Units or TPUs, which are specialized servers to accelerate the training of complex neural architectures.

model solution classification tensor processor
model solution classification tensor processor

After the model has finished its training, it is compressed and exported to JavaScript using TensorFlow, so that it can make predictions on a web page without any specialized hardware.

Finally, the website is built that includes recognition on static images and recognition on video stream, making use of our trained model.

neural networks solution detection model web

Demo Detection Model (with neural networks)


You can then test the detection model and identify its level of precision.


You can test the model in real time, activate your camera and see how it can recognize the objects it visualizes.