vehicle_1 Object Detection Dataset and Pre-Trained Model by Jadavpur University

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Explore Dataset

Here are a few use cases for this project:

Traffic Monitoring and Management: The vehicle_1 model can be used in traffic monitoring systems to analyze the flow of different vehicle types on roads. This information can help authorities optimize traffic light timing and lane usage, ultimately improving traffic conditions and reducing congestion.
Parking Optimization: By identifying vehicle types, the model can be used to optimize the allocation of parking spaces in parking lots. For example, allocating separate areas for autos or motorcycles can make it more efficient for drivers to find a suitable parking spot and help manage parking spaces better.
Road Safety and Accident Analysis: The vehicle_1 model can be employed to monitor road safety by detecting different vehicle types and their interactions with pedestrians, such as in the given example of people walking on a street at night. Insights gained from this analysis can help authorities improve road infrastructure, implement pedestrian safety measures, and minimize accidents.
Fleet Management: Logistics and transportation companies can use the vehicle_1 model to identify and track the types of vehicles in their fleet. This information can help businesses better allocate routes and schedules, optimize resource utilization and maintenance, and streamline their operations.
Public Transport Planning: By identifying the frequency of buses and auto rickshaws on city roads, the vehicle_1 model can be useful for public transport agencies in planning and optimizing their routes, schedules, and capacities. This can lead to better connectivity and increased efficiency in public transportation systems.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

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

@misc{
                            vehicle_1-5kkt7_dataset,
                            title = { vehicle_1 Dataset },
                            type = { Open Source Dataset },
                            author = { Jadavpur University },
                            howpublished = { \url{ https://universe.roboflow.com/jadavpur-university-tpoll/vehicle_1-5kkt7 } },
                            url = { https://universe.roboflow.com/jadavpur-university-tpoll/vehicle_1-5kkt7 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-09 },
                            }