Traffic Sign Detection and Warning System in real time with Custom Dataset & YOLOV8.

Fire detection using YOLOv8 involves utilizing a state-of-the-art object detection model to accurately identify fire in images or video feeds in real-time, leveraging its advanced capabilities to enhance early warning systems.

Machine Learning model

This project focuses on leveraging the YOLO-NAS model for Smoke Detection.

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained based on yolov10 with that custom dataset to indicate fractures in x-rays.

This project implements a YOLOv8 model to detect and classify various skin diseases from images. The model is trained on a dataset of labeled images and can identify different types of skin conditions in real-time.

Custom Yolov8x-cls edge model deployment and training to classify trash vs recycling.

The road sign recognition system of the Russian Federation, which uses an already prepared model for object detection and image segmentation in real time to improve road safety

A football analysis system built using YOLOv5, Supervision, OpenCV in Python.

From dataset https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1# a model is obtained, based on ML (SVR), with that custom dataset, to indicate drones detection

Contribution for Traffic Sign Detection and Warning System in real time with Custom Dataset & YOLOv8.

Soccer analysis using YOLOv8 & Supervision ByteTrack

A computer Vision project for avoiding potholes on road.

StreetSpecter is an innovative AI-driven solution designed to detect potholes and facilitate road maintenance.

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained using an adaptation of the project https://www.kaggle.com/code/nyachhyonjinu/yolov3-test instead any yolo model

This research work introduced various aspects from dataset preparation techniques to image pre-processing, model comparison, and performance analysis on the detection & instance segmentation of three microalgae class namely Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis.

From dataset https://universe.roboflow.com/test-svk7h/brain-tumors-detection/dataset/2 a model is obtained, based on yolov10 to indicate tumors in images of brains.

Repository documenting YOLOv5 training on Gazebo-simulated marine markers, with detailed Jupyter notebooks and stored model weights for enhanced object detection.

How to Train YOLOv8 Instance Segmentation on a Custom Dataset

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on ML (SVR), with that custom dataset, to indicate fractures in x-rays.