Real-time Object Detection Using TensorFlow.js

This lesson guides you through creating a real-time object detection application using TensorFlow.js and the COCO-SSD model. The lesson on the right detects objects in real time using a webcam, labels them, and displays the results directly in the browser.

Since the lesson utilizes the camera of a smart device (e.g., a laptop webcam or a smartphone front camera) for object recognition, the relevant functionality must be enabled for the lesson to proceed smoothly.

If you are using a desktop without a webcam, you may find it difficult to follow along. We recommend using a tablet or laptop instead.

TensorFlow.js is a JavaScript library designed to run and train machine learning (ML) models in both browser and Node.js environments.

It is based on Google’s TensorFlow and enables web applications to run deep learning models directly or convert existing models for use.

Since TensorFlow.js runs in the browser, it requires no additional setup and leverages WebGL for GPU-accelerated computations, ensuring fast processing.

Thanks to these features, it is widely used in fields such as image recognition, natural language processing (NLP), and biometric signal analysis.

The AI model used for object detection is COCO-SSD.

COCO-SSD is a pre-trained machine learning model for object detection, trained on the COCO (Common Objects in Context) dataset. It utilizes the SSD (Single Shot Multibox Detector) architecture to perform real-time object detection.

The COCO dataset contains 80 object classes, including people, cars, bicycles, dogs, and laptops. Compared to other deep learning-based object detection models like YOLO and Faster R-CNN, COCO-SSD is lighter and requires fewer computations, making it suitable for execution in the browser.