Dataset for Car Brand Detection

What is Car Brand Detection?

Car brand and model detection is a computer-vision based model/system that can provide incredible value while doing car monitoring/tracking and detection. Nowadays many industries use this kind of vehicle brand/ model detection such as transportation, security, marketing and law enforcement, etc. Car brand and model detection is an important part of many real-time applications for example automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc.

But in real-time scenarios, car brand and model detection 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.

To avoid all these issues at the model we need an effective car brand and model dataset that should be all these issue kinds of images. Because while training an AI-model, the model will learn all these patterns, so an AI-model can get the ability to predict all these kinds of issues in real-time.

Predictly created such an effective car brand and model dataset with many different kinds of brands and their models.

Details About the Car Model Detection Dataset

• • Input Type :- Image/Video
  • Annotation Type:- Bounding Box (Basically every car is bound by a box and labeled with its brand and model name)
  • Size of the dataset:- 4800 (but more data will be added in future)
  • Number of entities / labels :- 54
  • Labels:- Others, Maruti Suzuki Baleno, Maruti Suzuki Ertiga, Maruti Suzuki Swift, Maruti Suzuki Vitara Brezza, Maruti Suzuki Wagon R, Maruti Suzuki Alto, Maruti Suzuki Dzire, Maruti Suzuki Celerio, Maruti Suzuki Ciaz, Hyundai i20, Hyundai Venue, Hyundai Creta, Hyundai Verna, Hyundai Santro, Hyundai i10, Hyundai Elantra, Mahindra Scorpio, Mahindra Bolero, Mahindra XUV, Mahindra TUV, Mahindra Xylo, Mahindra Verito, Tata Nexon, Tata Harrier, Tata Tiago, Tata Hexa, Tata Safari Storme, Tata Nano, Tata Tigor, Tata Altroz, Toyota Innova, Toyota Glanza, Toyota Fortuner, Toyota Etios, Toyota Land Cruiser, Renault Duster, Honda Amaze, Honda City, Honda Civic, Honda Jazz, Ford Ecosport, Ford Endeavour, Ford Frigo, Ford Aspire, Ford Mustang, AUDI, BMW, Jeep, Skoda Rapid, Skoda Superb, Nissan Sunny, Nissan Micra, Mitsubishi.

Where and How to Collect the Data?

Methods:- Web Scraping, Data Collection, Data Extraction, Data Storage, Data Management, Data Preprocessing.

Technologies/Libraries used:- Python, Pandas, Selenium, BeautifulSoup, Requests, JSON, CSV, Opencv.

To create a car brand and model detection dataset we need to know to present the available different brands and models available. We can extract different kinds of car models and their brand images from different resources by using web scraping techniques. One more important note here is our trained model should be working on real-time scenarios so we should be extracted real-time images of different types of cars.

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 road with normal cameras these images are affected by weather conditions, shadows, etc. So we need to apply image preprocessing techniques to remove noise and enhancement techniques to improve the quality/resolution of the image.

One more process is to do is, we need to check if there any necessary images are available in the extracted images? Like all extracted images have one or more car images or not if the car is not present in any image we can remove all those images from extracted images.

How to Build a Resume NER Dataset?

Methods: Data Labeling, Data Visualization, Model Development, Machine Learning/Deep Learning, Model Evaluation, Active Learning.

Technology/Library used: Python, CSV, JSON, Pytorch, Numpy, TensorBoard, Fast.ai, Scikit-Learn, Matplotlib, Seaborn, and Image Annotation Tool.

Here you will know How Predictly performs different tasks to create Resume NER dataset effectively?

• • After car images extracting completed, we apply image preprocessing techniques and enhancement techniques to remove noise and improve the quality of images. Because the quality of images directly improves the performance of the model.
  • Collect car brands and model names and we use these names as labels of our car images dataset.
  • Our data is ready to turn into a dataset. For this dataset creation, we predict car brands and model labels for 20% of data by using computer vision state-of-the-art models such as YOLO, SSD. Results are sent to the annotators’ team for checking if all car labels are correctly predicted or not? If modifications are required, like are there any image needs bound box correctly or labels incorrect, annotators make changes.
  • We take this 20% of annotated data, build a computer vision model, and apply it to 10% of unseen data to predict brand and model labels.
  • And again, this 10% data was sent to the annotators’ team for cross-checking and combining the previous 20% data and this new 10% data, and again training a model using a total of 30% of annotated car image data. We need to repeat this process until complete annotation on 100% data.
  • After completing the 100% annotation process, we build a model like a car brand and model to apply to real-world problems/issues.

Where Can We Use the Resume NER Dataset?

Using this Car Brand And Model Detection dataset, we can build Car Brand and Model detection and then predict in real-time scenarios like at the traffic signals, while tracking cars with car brand and its model when someone steals your car, etc.

• • Upload images or videos or integrate cameras to implement in real-world scenarios.
  • The vehicle Detection model will detect if the image/video has a vehicle or not?
  • If a vehicle is present in the image/video then the result sent to the Vehicle Type Recognition model, which recognizes cars if the image/video has.
  • If a car present in the scene then the Car Brand Recognition model predicts the brand of the car and then the Car Model Detection system predicts the model of the particular car brand.