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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.
This is a seperate repository for local sensing and computation needed to run CINE. The main repository is located in the following link: https://github.com/fishnos/CINE
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.
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)
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 project presents a hybrid deep learning framework for automatic license plate recognition (ALPR), specifically tailored for Addis Ababa minibus taxis.