This project uses YOLOv11-nano to detect and classify solar panel conditions into six categories: Clean, Dust, Bird, Electrical, Physical, and Snow. The goal is to automate fault detection, reduce manual inspection time, and help maintain optimal solar panel efficiency using computer vision and deep learning.

An AI-powered Road Damage Assessment system using YOLOv11s & SAHI to detect potholes and cracks with high precision. Featuring a Streamlit dashboard for real-time analysis, it provides engineering reports based on IRC standards. Integrated with RAG technology to offer expert maintenance consulting derived from civil engineering manuals.

A prototype that detects speed signboards (20, 40, 60, STOP) using a custom-trained YOLOv11 model and automatically adjusts the movement of a small car using motor control. Designed for real-world scenarios like hospital, school, and highway zones.

YOLOv11n - based PPE detection model trained for iOS real-time inference via CoreML

This repository demonstrates how to use a pretrained YOLOv11 model from the Ultralytics library for image-based object detection.

Official implementation of An Explainable AI based Plant Disease Identification using a Two-Stage Detection-Classification Pipeline with YOLO and ECA-NFNet Framework, 28th International Conference on Computer and Information Technology (ICCIT, 2025)

End-to-End AI Visual Inspection System for manufacturing. Built with YOLOv11, OpenCV, and a Streamlit real-time analytics dashboard.

An AI-driven Adaptive Traffic Signal Control System (ATSCS) that replaces static timers with dynamic green-light phases. Utilizes YOLOv8 for real-time vehicle density estimation and multi-class classification, achieving 96.4% mAP. Optimized for low-latency inference (>30 FPS) on edge devices to reduce urban congestion and commuter wait times.

Smart ROV platform using Raspberry Pi for vision and remote access and Arduino for motor/sensor control.

Brain Tumor Classification & Segmentation using YOLOv11. Achieves 98.7% accuracy with a Dual-Pipeline "Gatekeeper" system. Deployed via Streamlit.

KhetRakshak is a high-performance, offline-first mobile application that automates expert entomology for local farmers. Powered by AndroidX CameraX, YOLOv11, and the OpenWeatherMap API, it features zero-latency AI bounding boxes, dynamic "Do Not Spray" weather heuristics, and an anti-flicker UX engine.

visionhub-ai is a Django-based application that demonstrates a powerful pipeline combining YOLOv11n-seg (instance segmentation) with bird classification and face recognition features.

CampusGuardAI: AI-powered real-time campus security system. Detects fights, trespassing, falls, sleeping, and phone use using YOLO & PoseNet. Sends instant alerts via dashboard, SMS & email. Privacy-first, no facial recognition.

A real-time object detection mobile application built with Flutter, integrating YOLOv12n on the COCO dataset via TensorFlow Lite for accurate, on-device detection with adjustable confidence thresholds.

This a ML based computer vision project.An automated construction site safety monitor using YOLOv11 to detect Personal Protective Equipment (PPE) in real-time.

Web Application that provides real-time American Sign Language to English (Text & Voice) translation.

Real-time multi-object detection and tracking system using YOLOv11 + ByteTrack. Built with FastAPI, React, and WebSocket streaming. Supports 5 detection modes: Traffic, Security, Wildlife, Retail, and Airport.

A real-time computer vision system deployed on a Raspberry Pi 5 that automatically detects motorcycle riders without helmets, reads their license plates via OCR, and logs violations to a cloud database.

An end-to-end YOLO-based computer vision pipeline for automated birdnest detection. Includes data validation utilities, model training scripts, and a FastAPI-based orchestration layer. Bird nest detection using YOLOv8

This project presents a hybrid deep learning framework for automatic license plate recognition (ALPR), specifically tailored for Addis Ababa minibus taxis.