object-detection-and-classification

Al-time object detection and tracking app using YOLOv8 and the SORT algorithm. It detects objects in images, videos, or webcam feeds, draws bounding boxes with class/ID, and tracks them across frames for consistent identification and visual output.

A system that detects scene text on traffic signs through images and videos

Utilizing synthetic images to enhance the automated recognition of small-sized construction tools

Crosswalk & Traffic Sign Recognition Dataset: A labeled collection of 1,800 images for autonomous driving. Features 14 classes including directional signs, obstacles, and road conditions. Initial models achieved a 78.1% mAP.

REST API to detect objects in images. Get labels, confidence scores, and bounding box coordinates. Powered by neural networks.

This repository contains the completed projects and practical implementations from my Computer Vision learning journey. It covers core concepts and advanced techniques, focusing on real-world applications using deep learning.

YOLOv8-based detection & automated counting of palm oil fruit bunches (TBS) — classifying Matang, Mentah, and Cacat in real-time. Industry collaboration project with PTPN, developed at Binus University. Exported to ONNX for edge deployment.

A new benchmark model for the automated recognition of a wide range of heavy construction equipment

Détection automatique du paludisme sur frottis sanguins — YOLO11m + classifieur NEG ResNet18. Pipeline 3 étapes : court-circuit NEG → détection YOLO+TTA → submission Zindi. mAP@0.5=0.866 — App Gradio déployée sur HF Spaces. PFM Licence IA, FSBM Casablanca