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AI & Machine Learning Case Study
Object & Face-Detection System
Research Project: Real-time computer vision boundary mapping and multi-class object localization.
30+ FPSfps
91.2% mAPprecision
Edge CPUhardware
Project Overview
A lightweight AI system designed for security cameras, autonomous systems, and edge nodes, locating and identifying human faces and multiple object classes.
The Problem
Standard CNN models are computationally heavy, requiring dedicated GPUs that are unavailable on IoT sensors and basic security camera feeds.
The Solution
Developed an optimized computer vision execution pipeline using OpenCV and pre-trained deep neural network architectures. Trimmed convolutional layers and set up multithreaded frame rendering queues.
System Architecture
Loads model configurations via OpenCV's DNN module. Frames are captured, downsampled, processed via MobileNet-SSD parameters, and overlaid with labels in real-time.
[Camera Frame Feed] ==> [OpenCV DNN Frame Decoder] ==> [MobileNet-SSD Model Processing]
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[Labeled Frame output]Key Engineering Features
- ◆Real-time bounding box tracking for multi-class targets.
- ◆Face boundary mapping resilient to angle shifts.
- ◆Zero network-dependency processing (fully local deployment).
Technologies Utilized
PythonTensorFlowOpenCVNumPyDeep Learning
Repository StateOpen Source (MIT)
Deployment LevelStaging / Verified Node