The Road Vehicle dataset contains Bangladesh road valencias images with annotation. There are two separate splits in this dataset, one contains train images and the other contains valid images. The author hopes it will be a great asset for autonomous vehicles and traffic management projects. The dataset is properly made for YOLO v5 real-time vehicle detection project.

The main objective for the use of an image dataset was to examine the five vehicle types of motor vehicles that were the most commonly used ones in Thailand (sedan, hatchback, pick-up, SUV, and van). The recording devices to collect the images were part of a video surveillance system located at Loei Rajabhat University in Loei province, Thailand. The collection process took place during the daytime for four weeks between July and December 2018. Two cameras were installed at the front gate of the university. However, a small number of van images was produced in the dataset compared to the number of images of the other four vehicle types. Because of this, the researchers decided to add other vehicle-type images such as those of motorcycles into the van group and changed the name of the group to "other vehicles" to increase diversity. Finally, the first dataset called "Vehicle Type Image Dataset (VTID)" had a total of 1,410 images that could be separated into vehicle types as follows; 400 sedans, 478 pick-ups, 129 SUVs, 181 hatchbacks, and 122 other vehicle images. Each image was collected using the 224x224 pixel resolution.

Car Semantic Segmentation Vehicle dataset

The dataset consists of a collection of car photos that have been manually annotated with masks. Each mask uses special colors to clearly mark the boundaries of different regions in the image.

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

DATASETS WITH VEHICLES :

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

Car Photos

The car images were collected in-house on city streets in clear weather conditions, with natural shooting angles. The dataset includes both passenger cars and commercial vehicles.

We at TrainingData offer image collection services tailored to your specific needs, delivering tens of thousands of images within a short time.

Semantic Segmentation

The masks represent a semantic segmentation of car photos. The following parts of the car are highlighted on the masks: - bumper - fog lights - radiator - license plate - door handle - and others.

The complete list of highlighted elements and the assigned colors of the car are available in the masks_info.csv file.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset

TrainingData also provides high-quality data annotation tailored to your needs.

keywords: 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

Context

This dataset was obtained from: https://github.com/nicolas-gervais/predicting-car-price-from-scraped-data/tree/master/picture-scraper

Welcome to _The Car Connection Picture Dataset!

It scrapes 297,000 pictures, of which around 198,000 unique URLs. Many of these are interior images, which are useless. You should have around 60,000 pictures in the end.

https://user-images.githubusercontent.com/46652050/71590299-ebd23f00-2af5-11ea-916f-f19ff6fad04a.jpg" alt="img">

This dataset has a collection of 383 raw images of Indian vehicles in different illumination conditions using Infrared Day/Night Camera. The dataset resembles the Indian highway toll collection plazas. The dataset will be useful in developing intelligent models for applications such as automated toll collection, number plate detection and recognition, driverless vehicles, suspicious vehicle traction, and traffic management.

The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cars196', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cars196-2.1.0.png" alt="Visualization" width="500px">

This dataset is an extremely challenging set of over 20,000+ original Construction vehicle images captured and crowdsourced from over 600+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

Dataset Features Dataset size : 20,000+ Captured by : Over 1000+ crowdsource contributors Resolution : 100% of the images are HD and above (1920x1080 and above) Location : Captured with 600+ cities accross India Diversity : Various lighting conditions like day, night, varied distances, view points etc. Device used : Captured using mobile phones in 2020-2022

Usage : Construction site object detection, workplace safety monitoring, self driving systems, etc.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

To download full datasets or to submit a request for your dataset needs, please drop a mail on sales@datacluster.ai . Visit www.datacluster.ai to know more.

This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

The Vehicle Detection 8 Classes Dataset is a robust collection designed for comprehensive object detection tasks, specifically concentrating on identifying and localizing vehicles. Comprising a substantial 8218 images, the dataset boasts an impressive 26098 annotated objects distributed among 8 distinct classes, encompassing vehicles like car, light_motor_vehicle, multi-axle, along with others such as auto, truck, bus, motorcycle, and tractor. With a focus on traffic analysis, each image within the dataset is equipped with boundary-box annotations, allowing for precise delineation and identification of vehicles, offering a valuable resource for applications related to traffic monitoring, object detection, and machine learning model training specifically tailored for traffic-related scenarios.

The Traffic Vehicles Object Detection dataset is a valuable resource containing 1,201 images capturing the dynamic world of traffic, featuring 11,134 meticulously labeled objects. These objects are classified into seven distinct categories, including common vehicles like car, two_wheeler, as well as blur_number_plate, and other essential elements such as auto, number_plate, bus, and truck. The dataset's origins lie in the collection of training images from traffic scenes and CCTV footage, followed by precise object annotation and labeling, making it an ideal tool for object detection tasks in the realm of transportation and surveillance.

Background Information

This dataset was curated and annotated by Ishaan Singh, a high school student from India.

The original dataset (v1) is composed of 166 images of various cars present in a junkyard. Training Set: 116 images, Validation Set: 33 images, Testing Set: 17 images.

The dataset is available under the Public License.

Ishaan ultimately used this dataset to create a "Drone Surveillance" system to count the cars using YOLOv5 & Deep SORT (Simple Online and Realtime Tracking with a Deep Association Metric) for a contest organized by ComputerVisionZone.

Here is a video of his final submission for the contest: Video of Ishaan's Final Model

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.

Dataset Versions

Version 1 (v1) - 166 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

The dataset contains JPEG images of vehicles. It has a total of 3858 images which is divided into 80 percent training set and 20 percent of testing set. This dataset can be used for vehicle make and model recognition.

The Boxy dataset contains almost 2 million annotated vehicles, e.g., cars and trucks, for object detection in 200,000 images.

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.

This dataset is designed for the detection of persons and cars in surveillance camera footage. It can be utilized for various useful applications, including:

• Security Systems: Enhancing security measures by accurately detecting and tracking persons and cars in real-time surveillance videos.
• Traffic Monitoring: Analyzing traffic patterns, estimating congestion levels, and optimizing traffic flow by detecting and counting cars on roads or at intersections.
• Safety Enhancement: Identifying potential hazards and ensuring public safety by detecting unauthorized access or suspicious activities involving persons and cars.
• Crowd Management: Monitoring crowded areas and public events to ensure safety, identify crowd density, and estimate crowd movement by detecting and tracking persons.
• Parking Systems: Optimizing parking lot management by detecting available parking spots and monitoring the entry and exit of vehicles.
• Smart Cities: Contributing to the development of smart city infrastructure by integrating the detection of persons and cars into intelligent systems for efficient urban planning and management.

This dataset is based on images collected from various sources, including:

https://universe.roboflow.com/radoslaw-kawczak/virat-ve02s

https://universe.roboflow.com/seminar-object-detection/cars-o1ljf

With this dataset, you can train and develop machine learning models capable of accurately detecting persons and cars, thus empowering surveillance and security systems with advanced object recognition capabilities.

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047

This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Roundabout (scenes)	Frames	Car	Truck	Cycle	Bus	Empty
1 (00001)	1,996	34,558	0	4229	0	0
2 (00002)	514	743	0	0	0	157
3 (00003-00017)	1,795	4822	58	0	0	0
4 (00018-00033)	1,027	6615	0	0	0	0
5 (00034-00049)	1,261	2248	0	550	0	81
6 (00050-00052)	5,501	180,342	1420	120	1376	0
7 (00053)	2,036	5,789	562	0	226	92
8 (00054)	1,344	1,733	222	0	150	222
Total	15,474	236,850	2,262	4,899	1,752	552
Data augmentation	x4	x4	x4	x4	x4	x4
Total	61,896	947,400	9048	19,596	7,008	2,208

Here are a few use cases for this project:

1. Insurance Assessment: This model can be used by insurance companies to automate the process of assessing car damage in insurance claims. By simply using photographs of the damaged vehicle, the model can identify the type and extent of damage, making the claim processing faster and more objective.

2. Automotive Repair Estimates: Car repair shops can use this model to get an approximate idea of the damage and therefore provide a more accurate cost estimate for their clients. It can also assist in identifying nonobvious damage.

3. Used Car Market Evaluation: This model can be used in used car platforms to evaluate the current condition of the cars listed for sale. By identifying existing damage, buyers can make more informed decisions and sellers can price their vehicles more accurately.

4. Law Enforcement and Road Safety: Traffic police and accident investigation teams can utilize this model to evaluate the types of damages after a road accident. It will assist in rebuilding the accident scenario, providing insights during investigations.

5. Auto-manufacturing Quality Control: Automobile manufacturers can use this model in their factories to automatically inspect new cars for any damage or misaligned/missing parts before they are dispatched from the factory, ensuring quality control.

This dataset is an extremely challenging set of over 8000+ original Fire and Smoke images captured and crowdsourced from over 1200+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

Dataset Features

Dataset size : 8000+ Captured by : Over 1200+ crowdsource contributors Resolution : 99% images HD and above (1920x1080 and above) Location : Captured with 800+ cities accross India Diversity : Various lighting conditions like day, night, varied distances, view points etc. Device used : Captured using mobile phones in 2021-2022 Usage : Vehicle detection, Autorickshaw detection, Self driving, Indian vehicles, Number Plate detection, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Aerial Multi-Vehicle Detection Dataset: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments. The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1] The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training. Subset Images Car Bus Truck Training 7239 200301 1601 6247 Validation 904 23397 193 727 Testing 905 24715 208 770 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing). [1] Makrigiorgis, R., 2021. Harpy Data Dataset. [online] Kios.ucy.ac.cy. Available at: https://www.kios.ucy.ac.cy/harpydata/ [Accessed 22 September 2022]. NOTE If you use this dataset in your research/publication please cite us using the following : Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442

The gigantic vehicle image data on the Internet can potentially promote more advanced object detection and classification models and algorithms. But organized, balanced, and valuable dataset remains a critical problem. We developed a new comprehensive Bangladesh road transport-based balanced image dataset called "Sorokh-Poth" is proposed, which harmonious with several CNN-based architectures such as YOLO, VGG-16, R-CNN, and DPM. Most of the dataset images were collected from a smartphone. The dataset comprises 9809 labeled and annotated images of 10 categories of vehicle images like Auto-rickshaw, bike, bus, bicycle, car, CNG, leguna, rickshaw, truck, and van.