4000 (244x224) images of vehicles on the highway. Images were taken from the top-down view in the same spot above the highway.

Vehicle detection is a computer vision dataset focused on identifying and locating vehicles within images or video frames. It is ideal for AI/ML applications in traffic monitoring, autonomous driving, and smart city projects.

Overview: The Common Vehicle Dataset offers a diverse collection of images, specifically curated to enable the identification and detection of four widely encountered vehicle types: bus, car, motorcycle, and truck. It aims to support machine learning and computer vision tasks, such as object detection, classification, and traffic surveillance.

Dataset Content: Classes:

Bus: Public transportation vehicles of various sizes. Car: Personal and passenger vehicles, including sedans and SUVs. Motorcycle: Two-wheeled motorized vehicles. Truck: Cargo vehicles for goods transportation, varying from light to heavy trucks. Applications:

• Vehicle detection and tracking in traffic monitoring systems.
• Autonomous driving model development.
• Vehicle counting and classification for urban planning.
• Accident prevention and traffic rule enforcement.

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb0789a0ec8075d9c7abdb0aa9faced59%2FFrame%2012.png?generation=1694606364403023&alt=media" alt="">

DATASETS WITH VEHICLES :

• License Plates - 1 209 438 OCR Plates
• Car Object Detection & Tracking
• Vehicle Images & Segmentation
• Parking Space Detection & Classification Dataset
• Videos around cars

Dataset structure

• images - contains of original images of roads
• masks - includes segmentation masks created for the original images
• annotations.xml - contains coordinates of the polygons, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Сlasses:

• road_surface: surface of the road,
• marking: white and yellow marking on the road,
• road_sign: road signs,
• car: cars on the road,
• background: side of the road and surronding objects

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa74a4214f4dd89a35527ef008abfc151%2Fcarbon.png?generation=1694608637609153&alt=media" alt="">

Roads Segmentation might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

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keywords: road surface, road scene, off-road, vehicle segmentation dataset, semantic segmentation for self driving cars, self driving cars dataset, semantic segmentation for autonomous driving, car segmentation dataset, car dataset, car images, car parts segmentation, self-driving cars deep learning, cctv, image dataset, image classification, semantic segmentation

We collected and annotated a dataset containing 105,544 annotated vehicle instances from 24700 image frames within seven different videos, sourced online under creative commons license. The video frames are annotated using DarkLabel tool. In the interest of reusability and generalisation of the deep learning model, we consider the diversity within the collected dataset. This diversity includes changes of lighting amongst the video, as well as other factors such as weather conditions, angle of observation, varying speed of the moving vehicles, traffic flow, and road conditions etc. The videos collected obviously include stationary vehicles, to perform the validation of stopped vehicle detection method. It can be noticed that the road conditions (e.g., motorways, city, country roads), directions, data capture timings and camera views, vary in the dataset producing annotated dataset with diversity. the dataset may have several uses such as vehicle detection, vehicle identification, stopped vehicle detection on smart motorways and local roads (smart city applications) and many more.

In the fourth quarter of 2024, there were around ***** million vehicles operating on roads throughout the United States. Almost **** million used vehicles changed owners in the U.S. between the fourth quarter of 2023 and the fourth quarter of 2024, while new registrations of vehicles came to about **** million units during that period. Automotive market disparities The number of licensed drivers had been steadily increasing up to just under ******* in 2023, but the automotive market has been impacted by economic developments over the past few years. The U.S. vehicle fleet is aging, reflected by the slow increase in the average vehicle age from **** years in 2018 to over ** years in 2024. This is in part due to market disparities. The average selling price of new vehicles has been increasing to nearly ****** U.S. dollars in 2024, up from under ****** in 2016. Used car prices have been declining after the chip shortages linked to the COVID-19 pandemic, reaching around ****** U.S. dollars in 2024. The majority of U.S. car owners earned more than ****** U.S. dollars per years, with the ****** to ****** income group owning over ** percent of the vehicles in use. The boom of the used vehicle market Close to ************* of new car buyers were born between 1946 and 1981, with Gen X being the leading consumers by age group for both the new and used vehicle market. Used light vehicle sales have been steadily increasing since 2010, representing well over double the size of the new light vehicle market in 2024. With a product range priced below new vehicle prices, used vehicles are gaining momentum in the United States. The average American household spends some ***** U.S. dollars on vehicle purchases annually, with consumers in income groups earning above 100,000 U.S. dollars per year spending above ***** dollars annually on car buying. Used vehicle financing options are naturally more affordable than new vehicle financing options, with an average monthly payment over *** dollars for loan payments for new vehicles.

Road Vehicle Images

## Overview

Road Vehicle Images is a dataset for object detection tasks - it contains Vehicles annotations for 2,999 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

United States US: Total Road Motor Vehicles: %: Goods Road Motor Vehicles data was reported at 53.359 % in 2019. This records an increase from the previous number of 52.030 % for 2018. United States US: Total Road Motor Vehicles: %: Goods Road Motor Vehicles data is updated yearly, averaging 42.797 % from Dec 1994 (Median) to 2019, with 26 observations. The data reached an all-time high of 53.359 % in 2019 and a record low of 34.325 % in 1994. United States US: Total Road Motor Vehicles: %: Goods Road Motor Vehicles data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; GOODS VEHICLES A goods road vehicle is any single road motor vehicle designed to carry goods (e.g. a lorry), or any coupled combination of road vehicles designed to carry goods (i.e. lorry with trailer(s), or road tractor with or without semi-trailer and with or without trailer). VEHICLES A road motor vehicle is a road vehicle fitted with an engine whence it derives its sole means of propulsion, which is normally used for carrying persons or goods or for drawing, on the road, vehicles used for the carriage of persons or goods.; GOODS VEHICLES Data refer to vehicles other than motorised two-wheelers, passenger cars and buses. VEHICLES Motor vehicle refers to any motorised (mechanically or electronically powered) road vehicle not operated on rails.

Road Vehicles Annotation

## Overview

Road Vehicles Annotation is a dataset for object detection tasks - it contains Road Vehicles annotations for 3,001 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Germany Number of Motor Vehicle on Road data was reported at 53,835,792.000 Unit in 2024. This records an increase from the previous number of 53,502,093.000 Unit for 2023. Germany Number of Motor Vehicle on Road data is updated yearly, averaging 48,564,119.000 Unit from Dec 2002 (Median) to 2024, with 23 observations. The data reached an all-time high of 53,835,792.000 Unit in 2024 and a record low of 43,785,202.000 Unit in 2006. Germany Number of Motor Vehicle on Road data remains active status in CEIC and is reported by Federal Motor Transport Authority. The data is categorized under Global Database’s Germany – Table DE.RA001: Auto Industry Statistics. 1) Before 2006, the figures included the vehicles which were temporarily out of service, e.g. in winter time cabriolets, campers etc. From 2006 onwards, the figures excluded vehicles which were temporarily out of service, hence, these values are no longer been comparable with previous data.

The dataset contains year-wise data on population, number of vehicles registered, the total road length of highways and all roads, and number of vehicles per 1,000 population and per 1,00 kilometers of road length.

This annotated dataset contains five different types of vehicles: cars, taxis, trucks, buses, and motorcycles taken from Unmanned Aerial Vehicles (UAVs), which we commonly know as drones. Mavic Air 2 Drone was used to take all the pictures in the Iraqi cities of Sulaimaniyah and Erbil.

Version 1 with Data Augmentation techniques applied (Brightness, Hue, and Noise)

Version 2 without any Data Augmentation techniques applied.

Forecast: Road Motor Vehicles in Germany 2024 - 2028 Discover more data with ReportLinker!

South Korea Total Road Motor Vehicles: %: Passenger Cars data was reported at 70.090 % in 2020. This records an increase from the previous number of 69.574 % for 2019. South Korea Total Road Motor Vehicles: %: Passenger Cars data is updated yearly, averaging 59.604 % from Dec 1994 (Median) to 2020, with 27 observations. The data reached an all-time high of 70.090 % in 2020 and a record low of 44.448 % in 1994. South Korea Total Road Motor Vehicles: %: Passenger Cars data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included. VEHICLES A road motor vehicle is a road vehicle fitted with an engine whence it derives its sole means of propulsion, which is normally used for carrying persons or goods or for drawing, on the road, vehicles used for the carriage of persons or goods.; VEHICLES Data do not include mopeds.

Some 284.6 million vehicles were registered in the United States in 2023. The figures include passenger cars, motorcycles, trucks, buses, and other vehicles. The number of light trucks sold in the U.S. stood at 12.4 million units in 2023. U.S. vehicle registrations The United States is one of the world’s largest automobile markets based on the number of new light vehicle registrations, with more than 15.5 million new light vehicle registrations in 2023. However, domestic production of automobiles stood at around 1.7 million units in 2023, which was under half the output recorded in 2016. At the same time, the United States imports a significant number of vehicles and vehicle parts from various countries, such as Japan, Mexico, and Canada. Leading car manufacturers in the United States The leading car manufacturers overall in the United States include the domestic heavyweights General Motors and Ford. With respect to car brands, the Ford brand clocked in at number one in 2024, selling around 2.1 million vehicles in the United States alone. The brand's holding company is the Ford Motor Company; it was founded by Henry Ford in 1903 in Dearborn, Michigan. The company pioneered in large-scale car manufacturing and introduced production methods such as the assembly line.

Forecast: Road Motor Vehicles in Mexico 2024 - 2028 Discover more data with ReportLinker!

Vehicle Detection Systems (VDS) Data includes traffic flow information such average vehicle speed, traffic flow and traffic concentration.

The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed by Eurostat in cooperation between the United Nations Economic Commission for Europe (UNECE) and the International Transport Forum (ITF) at OECD.

The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; its completeness varies from country to country.

Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide.

The Common Questionnaire collects aggregated annual data on:

• Railway transport
• Road transport
• Inland waterways transport
• Oil pipelines transport
• Road traffic - vehicle-kilometres
• Buses (and coaches)

For each mode of transport, the Common Questionnaire covers some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):

• Infrastructure (All modes)
• Transport equipment (Rail, Road and Inland Waterways)
• Enterprises, economic performance and employment (All modes)
• Traffic (Rail, Road and Inland Waterways)
• Transport measurement (All modes).

As its name suggests, the theme "Road traffic" focuses on "traffic" only, on road:

• of national vehicle stocks, on national and foreign territories, by type of road; and by vehicle category, fuel type and age. These data should rely on odometer readings;
• of national- and foreign-registered vehicles on national territories. These data should be issued from road censuses.

The theme “Buses and coaches” covers detailed information on road “traffic” (vkm) and “transport measurement” (passengers, passenger-km) performed by buses and coaches.

The data collection on Common Questionnaire was streamlined twice in the recent years:

• In 2013, among other changes, the collection of employment data was terminated;
• In 2019, among other changes, the collection of gas pipeline data was terminated, and the number of “Buses” indicators was reduced.

The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.

The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed by Eurostat in cooperation between the United Nations Economic Commission for Europe (UNECE) and the International Transport Forum (ITF) at OECD.

The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; its completeness varies from country to country.

Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide.

The Common Questionnaire collects aggregated annual data on:

• Railway transport
• Road transport
• Inland waterways transport
• Oil pipelines transport
• Road traffic - vehicle-kilometres
• Buses (and coaches)

For each mode of transport, the Common Questionnaire covers some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):

• Infrastructure (All modes)
• Transport equipment (Rail, Road and Inland Waterways)
• Enterprises, economic performance and employment (All modes)
• Traffic (Rail, Road and Inland Waterways)
• Transport measurement (All modes).

As its name suggests, the theme "Road traffic" focuses on "traffic" only, on road:

• of national vehicle stocks, on national and foreign territories, by type of road; and by vehicle category, fuel type and age. These data should rely on odometer readings;
• of national- and foreign-registered vehicles on national territories. These data should be issued from road censuses.

The theme “Buses and coaches” covers detailed information on road “traffic” (vkm) and “transport measurement” (passengers, passenger-km) performed by buses and coaches.

The data collection on Common Questionnaire was streamlined twice in the recent years:

• In 2013, among other changes, the collection of employment data was terminated;
• In 2019, among other changes, the collection of gas pipeline data was terminated, and the number of “Buses” indicators was reduced.

The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.