Specializing in Computer Vision, AI, ML & Deep Learning. Bridging the gap between hardware and intelligent systems with cutting-edge embedded solutions.
A committed, passionate, and talented engineer with technical expertise in Computer Vision, Artificial Intelligence, Machine Learning, and Deep Learning. Equipped with the capacity to lead and collaborate with others in a team.
My expertise lies in developing real-time vision systems, implementing neural networks on resource-constrained devices, and optimizing AI models for edge computing. I'm passionate about transforming innovative ideas into practical, intelligent embedded systems.
SLTC University
2021 - 2025
Specialized in Embedded Systems, AI, and Computer Vision
ESOFT Metro Campus
2017 - 2018
Diploma in IT & Diploma in English
Ananda College
Grade 1 - 13 | O/L & A/L
Engineering Technology, Science for Technology, Information and Communication Technology
6 Months Internship
Coursera / L&T Edutech
2025
Cisco Networking Academy
2025
Coursera / DeepLearning.AI
2023
Coursera / Google
2022
Coursera / UC San Diego
2022
Coursera / Google
2022
Coursera / Google
2022
Coursera / IBM
2022
Developing an autonomous firefighting robot that can move to different locations, detect fire automatically, and spray water without human intervention. All electronics and electrical system design developed through self-learning, including circuit design, power management, and system integration. Future phases will include camera-based navigation and vision systems for improved fire localization and autonomous movement.
View Project DetailsA hybrid ensemble system combining YOLOv11 for face detection, ArcFace (ResNet-50) for facial embedding extraction, and dual classification using FAISS k-NN (91.38% accuracy, 97.63% AUC) and MLP neural network (90.05% accuracy). Achieved real-time performance at 20+ FPS on GPU hardware with 200,046 face embeddings, demonstrating effective deep metric learning with vector database indexing.
View on GitHubIntelligent surveillance system integrating InsightFace ArcFace for face recognition (95% accuracy), custom YOLOv11 for weapon detection (88% precision, <480ms latency), and multi-agent chatbot (LangGraph + GPT-4o-mini + Gemini) supporting Sinhala, Tamil, and English. Features ESP32-based physical alerts and Twilio communication for real-time emergency response in Sri Lankan security applications.
View on GitHubAI-powered driver safety system combining YOLO11x for phone detection (94.2% accuracy) and MediaPipe for drowsiness monitoring, achieving 30+ FPS real-time processing. Features ESP32-based alerts (RGB LEDs, audio warnings), Blynk mobile app integration, and GPU optimization. Collaborative project demonstrating practical AI application for proactive road safety in Sri Lankan context.
View on GitHubIntelligent autonomous car system responding to hand gestures with active people tracking using advanced computer vision. Combines YOLO11x-pose estimation for gesture recognition, OpenCV for real-time processing, and ESP32 microcontroller for seamless human-robot interaction and vehicle control.
View on GitHubAI-powered gesture recognition system using neural networks trained with Google's Teachable Machine to control fan operations. Recognizes five distinct hand gestures for turning the fan on/off, increasing/decreasing speed, and setting specific speed levels. Demonstrates practical application of machine learning for smart home automation and gesture-based control systems.
View on GitHubPowerful and efficient face recognition system built with OpenCV, imgbeddings, and PostgreSQL for real-time face detection, registration, and recognition. Features database storage for embeddings, enabling scalable identity management and fast retrieval for real-time applications.
View on GitHubComprehensive face recognition system implementing cutting-edge MTCNN face detection and FaceNet recognition algorithms for robust, real-time identity verification. Supports multiple hardware configurations from standard webcams to Intel RealSense D435i depth cameras, suitable for both basic and advanced security applications.
View on GitHubDeep learning-powered breast cancer detection system using YOLOv8 object detection model to identify potential cancerous regions in medical images. Leverages computer vision to assist in early detection, significantly improving survival rates through AI-assisted medical image analysis and automated screening.
View on GitHubIntelligent coin detection system using YOLOv8 to accurately identify and categorize Sri Lankan currency coins across all denominations (Rs.1 to Rs.5000). Demonstrates practical application of computer vision for automated currency recognition, suitable for vending machines, cash handling systems, and retail automation.
View on GitHubWeb-based object detection application powered by YOLOv8 with Flask backend for image upload and processing. Features organized directory management for results, enabling efficient batch processing and analysis. Demonstrates full-stack integration of deep learning models for accessible computer vision applications.
View on GitHubAdvanced image segmentation system using YOLOv8x-seg trained on 1,000 annotated images via Roboflow over 50 epochs. Achieves accurate real-time apple detection and segmentation with precise object masks. Ideal for smart agriculture applications, quality control, and automated fruit sorting systems.
View on GitHubI'm always interested in discussing new projects, innovative ideas, or opportunities to be part of cutting-edge embedded AI solutions.