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

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

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.

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

Use machine learning to identify players, refs and football field markings.

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

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

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.

solar_panel_anomalies-Yolo. This is an essay that obtains a model to detect anomalies in solar panels using the roboflow file https://universe.roboflow.com/ron-zhyan/solar-panel-anomalies-hikbk-0joqn/dataset/1 as a dataset and a training with yolov11

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

From dataset https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1 a model is obtained, based on yolov10 to detect drones in images. Predictions from several models are used in cascade to obtain the optimal result.

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.

This repository demonstrates how to fine-tune YOLOv11n on multiple fire detection datasets. It provides a complete pipeline for combining multiple datasets from Roboflow, training a unified model, and evaluating its performance.

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://github.com/mahdi-darvish/YOLOv3-from-Scratch-Analaysis-and-Implementation instead any yolo model

This machine learning detects the severity level of motor-vehicle accidents between 11 different ranges.

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on yolov10, with that custom dataset, to indicate fractures in x-rays. The project uses 5 cascade models, if one does not detect fracture it is passed to another

Detection of fractures in images by obtaining the X and Y coordinates of the center of the fracture applying ML (SVR). It is applied to a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1 Compared to other tests using DL for the same set of data, much better precision and training time