Vehicle Detection Technology – Overview

what is vehicle detection technology

In today’s technology culture, data is the new oil. As a result of the impact of efficient data, performance benchmarks in terms of speed and accuracy have shifted. The improvement is seen because the data is processed using two industry buzzwords: Computer Vision (CV) and Artificial Intelligence (AI) (AI). For traffic vigilance systems, two technologies have enabled key duties such as object detection and tracking. The necessity for an effective algorithm to unearth hidden features grows as the number of features in an image grows. On the KITTI and COCO datasets, the Convolution Neural Network (CNN) model is created for single object detection on the urban vehicle dataset and YOLOv3 for multiple object detection. Performance metrics are used to analyze, assess, and tabulate model performance.

On traffic surveillance video, objects are monitored across frames using Simple Online Real-Time Tracking (SORT). The uniqueness of state-of-the-art networks is emphasized in this research. On a dataset of urban vehicles, efficient detection and tracking can be seen. Real-time, exact, precise identifications are provided by the algorithms, which are ideal for real-time traffic applications.


Vehicle detection processes on the road are used for vehicle monitoring, counts, average speeds of individual vehicles, traffic analysis, and vehicle categorization, and can be employed in a variety of situations.


Ways for Vehicle Detection


License Plate Recognition:
The ability to record photographic footage or photos from license plates and convert the optical data into digital information in real-time is known as license plate recognition (LPR).

LPR, also known as Automatic Number Plate Recognition (ANPR), is a widely utilized technology in Europe and the Americas for vehicle management operations such as Ticketless Parking (off-street and on-street), Tolling, ITS, stolen vehicle identification, smart billing, and many more applications.


License Plate Recognition adds digital license plate information (along with other data like vehicle direction and speed) to the mix, allowing operators to quickly capture and associate additional data about every vehicle passing through a control point:

  • The action itself: the vehicle’s time, location, direction, and speed.
  • The provenance of the vehicle: any restrictions or security alerts
  • The chauffeur: The driver’s license number, personal information, or contact information



Machine Learning:

Several methods for automatic image processing techniques have been proposed in several studies. Recently, research has focused on applying machine-learning or deep-learning algorithms to interpret traffic vision data, with one of the most notable instances being the use of You Only Look Once (YOLO). Because of its capacity to process many photos faster than traditional region-based convolutional neural networks, YOLO offers a lot of potential for real-time traffic monitoring (R-CNNs). Deep-learning techniques are used to help with this.


Artificial Inteligence (AI):

Artificial Intelligence plays a vital role in Vehicle Detection.  AI is on the edge. Meaning it’s built into the camera.

 Using target classification by human or vehicle. This enables our algorithm to know the difference between a dog/cat and a human.

 As well as common environmental factors such as rain and wind. So you can get alerts only when a Human / Vehicle is detected.


Folio3 to your Rescue: With our Vehicle Detection Solution, you can transform road traffic analysis.

Our traffic analysis system automates vehicle recognition and counting while also identifying the type/category of the vehicle, making it a must-have for road and safety. This allows you to easily analyze traffic/congestion and make informed decisions to reduce inefficiencies.
We have the flexibility to help you set up your solution the way you want it, whether it’s on-premises, in the cloud, or through API integration.



About Muhammad Imran

Muhammad Imran is a regular content contributor at Folio3.Ai, In this growing technological era, I love to be updated as a techy person. Writing on different technologies is my passion and understanding of new things that I can grow with the world.

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