Consuming the dataset made easy with a wide range of integration support starting from REST API to Cloud data storage support. With a few lines of code, you can easily consume and store the dataset at your end.
The dataset covers a wide range of images involving multiple scenarios, under different lighting conditions, different angles, major card brands which help in cut off the hassle of collecting data and augmenting it later for different scenarios.
Whether you train a model with the data having YOLO format or COCO format or even the classic PASCAL VOC XML format. We provide all types of export formats to help you train any kind of model with ease.
Car brand and model detection is a computer vision-based model/system that can provide incredible value while doing car monitoring/tracking and detection. The need for car brand and model detection has become prevalent in recent years due to the increase in security awareness for access control systems in parking lots, buildings, and restricted areas. In addition, it is an important part of many real-time applications, such as automatic vehicle inspection, traffic management, driver assistance systems, traffic behaviour analysis, traffic monitoring, etc.
To create a car model/brand detection dataset, we extracted different kinds of car models and their brand images from different resources using web scraping techniques. We extracted a sufficient amount of car images from different sites/resources; not all images are high-resolution images and not clear images. Images are captured at the traffic signals on the road with normal cameras; these images are affected by weather conditions, shadows, etc.
Because in real-time scenarios, this kind of system faces many challenges and issues because the system will effect by many factors such as e image acquisition, variations in illuminations and weather, occlusions, shadows, reflections, a large variety of vehicles, inter-class and intra-class similarities, addition/deletion of vehicles’ models over time, etc. So we need to give that kind of image dataset while building a model to teach different conditions.
It’s no secret that road traffic surveys provide various stakeholders with vital data for planning, designing roads, and setting maintenance priorities. Modern cars represent a new age for mobility and means of primary transport for us daily. With other surveillance aspects of urban life taking leaps forward, such as face recognition systems at airports and other security-related places, car identification is also considered a step forward in advanced surveillance systems. Also, it is one of the main parts of the traffic analysis system.
Like Vehicle, analysis begins with vehicle detection. Once the vehicle is detected, we can classify it based on its class (car, bus, truck, etc.), make (Toyota, Honda, Ford, etc.), colour (white, black, red, grey, etc.), or make and model (Toyota Corolla, Hando Accord, Ford Fusion, etc.). Self-driving vehicles and driver assistance, surveillance, traffic management, and law enforcement are a few applications taking advantage of automatic vehicle analysis.
So traffic analysis department uses a car make and model detection system by integrating it in their roadside CCTVs. Then CCTV works as supervisors, that those will monitor roads and count cars and as well as collect information about cars. Based on this information, the system will draw an analysis graph; this information is useful to maintain road safety and t plan better roads.
When insuring a vehicle, various factors go into determining the annual premium rate. While it is a common understanding that driving history, age, ZIP code, and credit score influence the cost of auto insurance, not several people think about the make and model of the vehicle when estimating how much it might cost to insure a car. The variety of cars being insured goes a long way in deciding the amount paid in premiums. Being close with the vehicle make and model and how they influence insurance rates can encourage car owners to save hundreds of dollars on insurance and comprehensive coverage.
Insurance agencies use car makes and models o find insurance convergence because insurance convergence is different for each client based on their car make and model. So insurances agencies don’t want to put their efforts into repetitive tasks. So they want to automate this process. Then they are creating applications with AI-based car make and model recognition models. So then the client will easily upload their car images and register to the insurance agencies; those agencies don’t need to check manually every uploaded image. The AI-based models automatically recognize car make and model and identify the insurance convergence cost.