Dataset

This dataset was created by Data Co-Lab

Contents

Car Poses

## Overview

Car Poses is a dataset for classification tasks - it contains Car Poses annotations for 7,690 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Sinister Cars

## Overview

Sinister Cars is a dataset for classification tasks - it contains Cars annotations for 2,452 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 [MIT license](https://creativecommons.org/licenses/MIT).

Description:

👉 Download the dataset here

The Damaged Traffic Signs Dataset focuses on Portuguese traffic signs captured under a variety of conditions, providing a comprehensive collection of images showcasing signs in both their regular state and with various forms of damage or deterioration. The dataset is designed to support research and development efforts in the areas of traffic sign detection, classification, and damage recognition for AI and machine learning models.

Dataset Features:

This dataset includes traffic signs that have undergone several types of wear and tear, presenting realistic conditions encountered on the road. The goal is to offer a broad set of visual challenges for training models that can accurately identify and classify traffic signs, even in less-than-ideal circumstances.

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Conditions Captured:

The dataset encapsulates a range of deteriorations and flaws, including but not limited to:

Discoloration: Signs with faded colors due to prolonged exposure to sunlight or environmental factors.

Paint Peeling: Signs with paint coming off, making it harder to detect their design elements.

Scratches and Surface Damage: Signs damaged by vehicles or debris, which obscures the visibility of key information.

Bending or Warping: Traffic signs that are bent or physically distorted, altering their appearance from the standard design.

Rust: Older signs suffering from rust, particularly those made from metal.

Graffiti or Stickers: Signs vandalized with graffiti, stickers, or other forms of defacement.

Partial Occlusion: Signs partially blocked by objects such as tree branches, poles, or other obstructions.

Design and Manufacturing Flaws: Some images capture signs with design flaws or imperfections resulting from poor manufacturing.

Reflections and Glare: Signs affected by reflections from sunlight or vehicle headlights, which create visibility challenges.

Blurring: Signs captured under motion or with out-of-focus camera settings, simulating real-world challenges in detection.

Environmental Wear: The dataset also captures signs under various environmental factors such as rain, dirt, and dust.

Additional Information:

Resolution: Images are captured at various resolutions, ensuring diversity in image quality.

Weather Conditions: The dataset includes images taken in different weather conditions, such as sunny, rainy, and foggy days, which add further complexity.

Lighting Conditions: Photographs were taken under a mix of lighting conditions, including bright daylight, evening, and low-light situations.

Applications:

This dataset is an excellent resource for developing and training machine learning models for:

Traffic sign recognition and detection systems.

Damage classification for traffic sign maintenance and road safety assessment.

Autonomous vehicle navigation and obstacle detection.

Government or municipal agencies for traffic infrastructure analysis.

This dataset is sourced from Kaggle.

Accident Detection

## Overview

Accident Detection is a dataset for object detection tasks - it contains Vehicle Accidents annotations for 933 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).

CSV that contains 1.000 records of wildlife–vehicle collisions (WVC) on interurban roads in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to contact me and I will forward it to you.

Data source of each WVC record is the Spanish General Directorate of Traffic (DGT), but the dataset has been enhanced by the integration of other sources: OpenStreetMap (OSM), Global Biodiversity Information Facility (GBIF), the National Geographic Institute of Spain (IGN), State Meteorological Agency (AEMET).Therefore, each record describes an accident by the following fields:

• id_num (int8): the unique identifier for an accident. • ind_accda (int8): a binary variable for property damages involved or not (encoded). • nombre_ind_accd (str): a statement for property damages involved or not (decoded). • ind_acciv (int8): a binary variable for personal damages involved or not (encoded). • nombre_ind_acciv (str): a statement for personal damages involved or not (decoded). • total_mu30df (int8): the total number of deaths from the accident. • total_hg30df (int8): the total number of injured with hospitalization from the accident. • total_hl30df (int8): the total number of injured without hospitalization from the accident. • fecha_accidente (date): the reported date of the collision, following ISO 8601 date-time standard. • hora_accidente (str): the reported hour of the collision in 24-hour notation. • mes_1f (int8): the month as integer of the event date (encoded). • nombre_mes (str): the month name of the event date (decoded). • anyo (int8): the four-digit year of the event date. • ccaa_1f (int8): the autonomous region code from INE where accident is registered (encoded). • nombre_ccaa (str): the name of the autonomous region where accident is registered (decoded). • provincia_1f (int8): the province code from INE where the accident is registered (encoded). • nombre_provincia (str): the province name where the accident is registered (decoded). • cod_municipio (int8): the municipality code from INE where the accident is registered (encoded). • nombre_municipio (str): the municipality name where the accident is registered (decoded). • carretera (str): the road attending to the national road numbering system in Spain where the accident is located. • km (float): the kilometre point of the road where the accident is located. • sentido_1f (int8): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded). • nombre_sentido (str): the vehicle’s direction of traffic reported when the accident occurred (decoded). • tipo_via_3f (int8): the type of road as integer attending to the project road classification (encoded). • nombre_tipo_via (str): the type of road description attending to the project road classification (decoded). • titularidad_via_2f (int8): the road ownership type as integer (encoded). • nombre_titularidad_via (str): the road ownership type description (decoded). • tipo_animal_1f (int8): the animal species involved in the accident as integer (encoded). • nombre_tipo_animal_1f (str): the animal species name involved in the accident (decoded). • tipo_animal_2f (int8): the reported animal type of breeding as integer (encoded). • nombre_tipo_animal_2f (str): the type of animal breeding description (decoded). • longitud (float): the length of the accident location coordinate in decimal degrees. • latitud (float): the latitude of the accident location coordinate in decimal degrees. • geom (geometry): geometry from latitude and longitude position. Developed for this project. • dia_semana (int8): the integer day of the week when the accident occurred (encoded). • nombre_dia_semana (str): the name of the day when the accident occurred (decoded). • tipo_dia (str): the category name of the day type to separate weekday from weekend (decoded). • parte_dia (str): the part name of the day when the accident is registered including day, night and the transitions. • luna (int8): the portion of illuminated moon surface represented as an integer value from 0 to 100. • prec (float): the daily rainfall measurement of the event day based on pluviometric days. • tmin (float): the minimum temperature in Celsius of the event day. • tmed (float): the average temperature in Celsius of the event day. • tmax (float): the maximum temperature in Celsius of the event day. • sol (float): the accumulated sun hours of the event day. • uso_suelo (str): the main land usage of the accident area. • altitud (float): the altitude in meters above sea level. • pendiente (float): the slope median value of a 30 meters buffer around the accident location. • taxonkey (str): a taxon key from the GBIF backbone. • imd_total (float): the average daily traffic intensity of the accident year. • maxspeed (int): the maximum speed of the road section where the reported collision.

The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).

This dataset is the output of the wildlife–vehicle collision analysis and the code repository is available on GitHub.

Statistical classification of the application for vehicle traffic accident identification cases in this city (since January 2020)

This metadata record was generated by the mCLOUD and summarizes all individual metadata records of this data series. Data on traffic accidents in Schleswig-Holstein ## Description of the fields - ID - Consecutive number of the accident (one data record per accident) - ULAND - State, here only 01 = Schleswig-Holstein - UREGBEZ - always 0 - UKREIS - district - UGEGEINDE - municipality - UJAHR - accident year - UMONAT - accident month - USTUNDE - accident hour - UWECHENDAY - weekday: 1 = Sunday, 2 = Monday, 3 = Tuesday , … 6 = Saturday - UKATEGORIE - Accident category (criterion for classification is the most serious consequence of the accident): 1 = accident with fatalities, 2 = accident with serious injuries, 3 = accident with minor injuries - UART - type of accident: 1 = collision with approaching/stopping/stationary vehicle, 2 = collision with preceding/waiting vehicle, 3 = collision with vehicle traveling sideways in the same direction, 4 = collision with oncoming vehicle, 5 = collision with turning/crossing vehicle, 6 = collision between vehicle and pedestrian , 7 = impact with lane obstacle, 8 = lane departure to the right, 9 = lane departure to the left, 0 = other type of accident - UTYP1 - type of accident: 1 = driving accident, 2 = turning accident, 3 = turning/crossing accident , 4 = crossing accident, 5 = accident caused by stationary traffic, 6 = accident in parallel traffic, 7 = other accident - ULICHTVERH - lighting conditions: 0 = daylight, 1 = twilight, 2 = darkness - IstRad - accident with bike : 1 if at least one bicycle was involved in the accident - IstPKW - accident involving a car: 1 if at least one passenger car was involved in the accident - IstFuss - accident involving a pedestrian: 1 if at least one pedestrian was involved in the accident was involved - IstKrad - Accident involving a motorcycle: 1 if at least one motorcycle, e.g normal body and a total weight of more than 3.5 t, a truck with tank support or special body, a tractor unit or another tractor unit was involved - IsOther - Accident with others: 1 if at least one means of transport not mentioned above (e.g. bus or train) was involved - USTRSTATE - Road condition: 0 = dry, 1 = wet/damp/slippery, 2 = slippery - LINREFX - The geo-coordinates of the accident location on the road section (UTM coordinate of the ETRS89 reference system, zone 32N) - LINREFY - XGCSWGS84 - The geocoordinates of the accident site on the road section (geographical coordinates in decimal degrees of the WGS84 reference system) - YGCSWGS84 ## Data origin This is an extract from the accident data for Germany https:// unfallatlas.Statisticsportal.de/_opendata2021.html Filtering was done for the entries where ULAND=01 is. In the geo-coordinates, the comma has been replaced by a decimal point. Further explanations of the traffic accident data can be found on the page of the accident atlas of the statistical offices of the federal and state governments.

CSV that contains 1.000 records of interurban road accidents with casualties in Spain between 2016 and 2021. If you are interested in the whole country dataset, please do not hesitate to contact me and I will forward it to you.

Data source of each record is the Spanish General Directorate of Traffic (DGT) and each row describes an accident by the following fields:

• secuencial (int): the unique identifier for an occurrence record in DGT. • anyo (int): the four-digit year of the event date. • mes (int): the month as integer of the event date (encoded). • nombre_mes (str): the month name of the event date (decoded). • dia_semana (int): the integer day of the week when the accident occurred (encoded). • nombre_dia_semana (str): the name of the day when the accident occurred (decoded). • hora (int): the reported hour of the collision in 24-hour notation. • cod_provincia (int): the province code from INE where the accident is registered (encoded). • nombre_provincia (str): the province name where the accident is registered (decoded). • cod_municipio (int): the municipality code from INE where the accident is registered (encoded). • nombre_codigo_municipio (str): the municipality name where the accident is registered (decoded). • isla (str): an integer value to identify a Spanish island if the accident occurred out of the peninsula (encoded). • nombre_isla (str): the island name if applicable to the accident (decoded). • zona (int): an integer value to identify type of road (encoded). • nombre_zona (str): the name of the road type (decoded). • zona_agrupada (int): an integer value to group the road types in urban or interurban (encoded). • nombre_zona_agrupada (str): the group name of the road types (decoded). • carretera (str): the road attending to the national road numbering system in Spain where the accident is located. • km (int): the kilometre point of the road where the accident is located. • sentido_1f (int): the vehicle’s direction of traffic reported as integer when the accident occurred (encoded). • nombre_sentido (str): the vehicle’s direction of traffic reported when the accident occurred (decoded). • titularidad_via (int): the road ownership type as integer (encoded). • nombre_titularidad_via (str): the road ownership type description (decoded). • tipo_via (int): the type of road as integer attending to the project road classification (encoded). • nombre_tipo_via (str): the type of road description attending to the project road classification (decoded). • tipo_accidente (int): an integer value to identify the collision type and traffic context (encoded). • nombre_tipo_accidente (str): the description of the collision type and traffic context (decoded). • total_mu24h (int): the total number of fatalities registered in the accident, computed to 24 hours. • total_hg24h (int): the total number of hospitalised casualties recorded in the accident, counted over 30 days. • total_hl24h (int): the total number of non-hospitalised casualties recorded in the accident, computed to 24 hours. • total_victimas_24h (int): the total number of casualties (fatalities + hospitalised injured + non-hospitalised injured) recorded in the accident, computed over 24 hours. • total_mu30df (int): the total number of fatalities recorded in the accident, computed over 30 days. • total_hg30df (int): the total number of hospitalised casualties recorded in the accident, counted over 30 days. • total_hl30df (int): the total number of non-hospitalised casualties recorded in the accident, counted over 30 days. • total_victimas_30df (int): the total number of casualties (killed + injured in hospital + injured not in hospital) recorded in the accident, counted over 30 days. • total_vehiculos (int): the total number of vehicles involved recorded in the accident. • tot_peat_mu24h (int): the total number of pedestrian fatalities recorded in the accident, computed over 24 hours. • tot_bici_mu24h (int): the total number of cyclists killed recorded in the accident, computed to 24 hours. • tot_ciclo_mu24h (int): the total number of scooter riders killed recorded in the accident, computed on a 24-hour basis. • tot_moto_mu24h (int): the total number of motorcyclist fatalities recorded in the accident, computed on a 24-hour basis. • tot_tur_mu24h (int): the total number of car drivers and passengers killed recorded in the accident, counted to 24 hours. • tot_furg_mu24h (int): the total number of van drivers and passengers killed recorded in the accident, counted over 24 hours. • tot_cam_menos3500_mu24h (int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 24 hours. • tot_cam_mas3500_mu24h (int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 24 hours. • tot_bus_mu24h (int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 24 hours. • tot_otro_mu24h (int): the total number of drivers and passengers of vehicles not classified in the above types killed in the accident, counted to 24 hours. • tot_sinespecif_mu24h (int): the total number of drivers and passengers of vehicles of unspecified type killed in the accident, counted over 24 hours. • tot_peat_mu30df (int): the total number of pedestrian fatalities recorded in the accident, computed to 30 days. • tot_bici_mu30df (int): the total number of cyclists killed recorded in the accident, counted over 30 days. • tot_ciclo_mu30df (int): the total number of scooted riders killed recorded in the accident, counted over 30 days. • tot_moto_mu30df (int): the total number of motorcyclist fatalities recorded in the accident, counted over 30 days. • tot_tur_mu30df (int): the total number of drivers and passengers of passenger cars killed in the accident, counted over 30 days. • tot_furg_mu30df (int): the total number of van drivers and passengers killed recorded in the accident, counted over 30 days. • tot_cam_menos3500_mu30df (int): the total number of drivers and passengers of trucks ≤ 3,500 kg killed in the accident, counted over 30 days. • tot_cam_mas3500_mu30df (int): the total number of drivers and passengers of trucks > 3,500 kg killed in the accident, counted over 30 days. • tot_bus_mu30df (int): the total number of bus drivers and passengers fatalities recorded in the accident, counted over 30 days. • tot_otro_mu30df (int): the total number of drivers and passengers of vehicles of types not classified in the above killed in the accident, counted over 30 days. • tot_sinespecif_mu30df (int): the total number of drivers and passengers of unspecified type vehicles killed in the crash, counted over 30 days. • nudo (int): an integer to identify whether the collisions occurred in an road junction or not (encoded). • nombre_nudo (str): the description to identify whether the collisions occurred in an road junction or not (decoded). • nudo_info (int): an integer that represents the type of road junction in which the collision occurred (encoded). • nombre_nudo_info (str): the description of the type of road junction in which the collision occurred (decoded). • carretera_cruce (str): the road attending to the national road numbering system in Spain where the accident is located. • priori_norma (int): an integer to identify if the road junction priority is determined by generic traffic rule (encoded). • nombre_priori_norma (str): the text to identify if the road junction priority is determined by generic traffic rule (decoded). • priori_agente (int): an integer to identify if the road junction priority is determined by agent (encoded). • nombre_priori_agente (str): the text to identify if the road junction priority is determined by agent (decoded). • priori_semaforo (int): an integer to identify if the road junction priority is determined by traffic light (encoded). • nombre_priori_semaforo (str): the text to identify if the road junction priority is determined by traffic light (decoded). • priori_vert_stop (int): an integer to identify if the road junction priority is determined by vertical stop sign (encoded). • nombre_priori_vert_stop (str): the text to identify if the road junction priority is determined by vertical stop sign (decoded). • priori_vert_ceda (int): an integer to identify if the road junction priority is determined by vertical yield sign (encoded). • nombre_priori_priori_vert_ceda (str): the text to identify if the road junction priority is determined by vertical yield sign (decoded). • priori_horiz_stop (int): an integer to identify if the road junction priority is determined by horizontal stop signal (encoded). • nombre_priori_horiz_stop (str): the text to identify if the road junction priority is determined by horizontal stop signal (decoded). • priori_horiz_ceda (int): an integer to identify if the road junction priority is determined by horizontal yield sign (encoded). • nombre_priori_horiz_ceda (str): the text to identify if the road junction priority is determined by horizontal yield sign (decoded). • priori_marcas (int): an integer to identify if the road junction priority is determined by road markings (encoded). • nombre_priori_marcas (str): the text to identify if the road junction priority is determined by road markings

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 30 November 2023. Fatal crashes to 30 November 2023. Non-fatal, hospitalisation, medical treatment and minor injury crashes to 30 June 2023 and property damage only crashes to 31 December 2010.

_Fatal, Hospitalisation, Medical treatment and Minor injury: _

This dataset contains information on crashes reported to the police which resulted from the movement of at least 1 road vehicle on a road or road related area. Crashes listed in this resource have occurred on a public road and meet one of the following criteria:

• a person is killed or injured, or
• at least 1 vehicle was towed away, or
• the value of the property damage meets the appropriate criteria listed below.

_Property damage: _

1. $2500 or more damage to property other than vehicles (after 1 December 1999)
2. $2500 or more damage to vehicle and/or other property (after 1 December 1991 and before 1 December 1999)
3. value of property damage is greater than $1000 (before December 1991).

_Please note: _

• This data has been extracted from the Queensland Road Crash Database.
• Information held in the Road Crash Database on events occurring within the last 12 months is considered preliminary as investigations into crashes can take up to 1 year to finalise.
• Property damage only crashes ceased to be reported/recorded by Queensland Police Service after 31 December 2010.
• These crash location coordinates reference the current Australian geodetic datum is GDA2020 (previously it was GDA94).

In New South Wales, the wearing of seat belts was compelled by a new regulation (Regulation 110F) under the Motor Traffic Act, 1909, in the following terms: "No person shall, while occupying a seat position in a motor car to which a seat belt has been fitted for the seat position, drive or travel, upon a public street, in that motor car unless he is wearing that belt and the belt is properly adjusted and securely fastened." The penalty for an offence under this regulation was set at $20 (Australian). Exemptions were set down for various categories of vehicle use (such as house-to-house delivery work) and vehicle occupant (such as children of under the age of eight years).

In 2023, road accidents involving cars in Metro Manila in the Philippines accounted for about 54 percent of the total road accidents involving vehicles. This was followed by motorcycle accidents, which accounted for about 22 percent of the total vehicular accidents in the area.

Accuracy: The City of Ottawa provides this information in good faith but provides no warranty, nor accepts any liability arising from any incorrect, incomplete, or misleading information or its improper use.Collision Data Accuracy: Each reportable collision occurring on public roadways is sent to the City of Ottawa and is validated at least once. Approximately 50% of the records are validated once again by a senior staff. Additionally, many queries are run on the database looking for errors.X & Y Accuracy: Collisions are pinned according to information provided by the officer on the Motor Vehicle Collision Report. In some cases, insufficient information was provided, and the collision location is an estimate.Update Frequency: MonthlyAttributes:DateTimeLocation (RD1 @ RD2 or RD from RD 1 to RD 2)Location Type (Intersection, non-intersection, at/near private driveway) Classification of collision (non-fatal, fatal, property damage only)Initial impact type (Angle, turning movement, rear-end…)Road surface condition (Ice, wet, dry snow...)Environment (Clear, rain, snow…)Light (daylight, dawn, dusk…)Traffic control (stop, traffic signal, no control…)Number of VehiclesNumber of PedestriansNumber of BicyclesNumber of MotorcyclesMax Injury (Highest injury level in the collisions)Number of InjuriesNumber of Minimal Injuries (Person did not go to hospital when leaving the scene of the collision)Number of Minor Injuries (Person went to hospital and was treated in the emergency room, but not admitted)Number of Major Injuries (Person admitted to hospital. Includes person admitted for observation. This could be either life threatening or non-life threatening)Number of Fatal Injuries (Person killed immediately or within 30 days of the motor vehicle collision)X and Y Coordinate (MTM Zone 9, NAD83)Latitude and longitude (WGS1984)Contact: Transportation Data Collection & Analytics

Relationship between the departure speeds and the departure angles.