Vehicle made and color detection

Vehicle made and color detection
Vehicle made and color detection

Is Vehicle detection necessary? According to Punjab police more than five thousand cases of vehicle theft were reported in 2018 in Pakistan. Every year there are nearly seven lakh car thefts in US, as reported by FBI. Authorities say that many of the stolen cars are break apart to sale each individual part in the But there’s another way of getting complete benefit without making much effort on chopping cars. You are curious about second way? How to detect a suspicious car?

VIN cloning for vehicle detection:

Second way for vehicle detection is known as VIN cloning, it is more sophisticated and profitable scheme for thieves. According to the National Crime Bureau, vehicle identification number cloning is the more fatal way of hiding the identity of the car. 

Vehicle made and color detection

In a statement about the performance of police force in 2018, Islamabad police claimed that they had arrested more than eighty criminals who had stolen 108 cars from different areas. Similarly, dozens of motorbike are recovered in 2018.  If your car is also stolen, your car might be on roads with another number plate on it.

How VIN cloning works

Sad to say, but initially a thief will steal your car then they will go to the market to get vehicle identification number of similar car. After getting fake number plate from market they will install it on their stolen vehicle. That’s a really big issue for innocent sufferers as they are unable to identify their vehicle.

Mike Cella was one of the victims, a past high school teacher and football coach of his school team. He went to exchange his 2008 GMC Yukon car to get new one last November, but he received shocking news about his car instead. The dealer in Massachusetts said he can’t give him the full value for his truck because a Carfax report was showing that the truck has been in four different accidents.

Finally it was found that according to West Palm Beach County Sheriff’s office nearly seven years ago, two thieves stole a 2007 GMC Yukon Denali that looks similar to Mike Cella’s car. Later on, thieves copied the VIN number of his car and they placed it on the stolen Yukon Denali to hide the theft. The car was exchanged several hands. After seven years of theft it had been in four different accidents, according to its Carfax report.

How we are helping

We are generating a system for make and model detection of the vehicle along with vehicle identification number. The system will analyse the vehicle appearance with the help of make, model and color detection. The vehicle will cross check the make and color of the vehicle to verify that number of the vehicle actually belong to same vehicle. Some basic points of the proposed system are,

  • Recognize the vehicle from frontal and back view.
  • Recognize different individual categories of the vehicles that includes Bus, Car and Truck
  • Recognize multiple different makes of vehicles that comprises Toyota, Honda, Suzuki, Nisan and Hyundai.
  • Recognize all recent models of each make.
  • It would be able to detect from distant images of cars by maximum 200 meters.

The system will be helpful for,

  • Fake number plates detection by checking the make and color of the vehicle.
  • Non-standard number plate detection.
  • Helpful in surveillance of specific vehicle.
  • It can be useful in pre-emptive security threats monitoring.
  • It can be used in toll systems for automatic toll tax collection.
  • It can be helpful in self-driving cars.

Make and made vehicle detection can be considered as object detection. The system will be able to find the make of the vehicle based on complete vehicle and small parts: lights, logo and bumper. Color is detected from complete vehicle image.


They proposed system will be implemented in current state-of-the-art paradigm. We will use deep regional convolution neural network for detecting the parts of the vehicles and for color and make classification simple convolution neural networks would be used.


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