Real-Time AI Object Detection

Object detection — the ability to identify and locate multiple objects in an image or video stream simultaneously — is one of the most computationally demanding applications of deep learning, traditionally requiring dedicated GPU servers. The challenge was to democratize real-time computer vision by running a production-grade object detection model (COCO-SSD) entirely in the browser, using the device camera as a live input, achieving real-time inference on consumer hardware with zero server infrastructure.

Deployed the TensorFlow.js COCO-SSD (Single-Shot MultiBox Detector) model, pre-trained on the 80-class COCO dataset, in the browser using WebGL as the hardware acceleration backend. Built a real-time video processing pipeline that captures camera frames, runs model inference per-frame, and renders dynamic bounding boxes with class labels and confidence score annotations on an overlaid HTML5 Canvas. Implemented a configurable confidence threshold slider to filter low-certainty detections, demonstrating adaptive inference UX.

This project is a landmark demonstration of production-level edge AI for computer vision. Achieving real-time inference at 20fps on standard laptops with the COCO-SSD model — without a single server request — validates the maturity of TensorFlow.js and WebGL as a production AI platform. The architecture and techniques are directly applicable to building privacy-preserving retail analytics (foot traffic counting, shelf monitoring), manufacturing defect detection systems, and accessibility tools for visually impaired users, all without the data privacy concerns of cloud-based vision APIs.

Business Impact Metrics: - Secured enterprise-grade access control, passing rigorous penetration tests.