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 in 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.
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 the present 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 it, 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.
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?
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.
If a car is 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.
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