Saad Elhidaoui

Truck segmentation

Instance Segmentation

Truck segmentation Computer Vision Project

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1100 images
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This project is part of my Master's thesis in Applied Mathematics and Computer Science. The title of the thesis is "Deep Learning for Automatic Detection of Trailers and Tankers and Categorization of their Activities." The objective of this project is to develop a deep learning system that can automatically detect trailers and tankers in images or video feeds and classify their activities. This research has important applications in various fields, including transportation logistics, security, and surveillance.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            truck-segmentation-tzuv1_dataset,
                            title = { Truck segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { Saad Elhidaoui },
                            howpublished = { \url{ https://universe.roboflow.com/saad-elhidaoui/truck-segmentation-tzuv1 } },
                            url = { https://universe.roboflow.com/saad-elhidaoui/truck-segmentation-tzuv1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { feb },
                            note = { visited on 2024-03-04 },
                            }
                        

Connect Your Model With Program Logic

Find utilities and guides to help you start using the Truck segmentation project in your project.

Last Updated

9 days ago

Project Type

Instance Segmentation

Subject

tank-truck-or-tipper-truck

Classes

Trailer pneu tank-truck tipper-truck

Views: 236

Views in previous 30 days: 31

Downloads: 40

Downloads in previous 30 days: 6

License

Public Domain

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