Tire Recognition meets Machine Learning

The Issue they were facing was tire sorting and distribution within their wholesale centers. Frank’s tire catalogue included 80 tire manufacturers and over 20 000 unique tire models, with 5digit number of tires moving daily. Aside from the risk of human error during sorting upon arrival, there was a risk of human error when sorting prior to shipping out.
Our approach included computer vision and conveyor belts, and the job was performed by five people from our side.

Modus Operandi

Step 1. Data Extraction

Our goal was to EXTRACT THE DATA from the
tire. Data that we wanted to extract was the
following:

• Manufacturer
• Model
• Dimensions
• Load/Speed Index
• DoT number
• Special marks (e.g. Ⓐ - circled A representing Audi original part)

To illustrate, take a look at the tire. If you are not in the tire business, this might be a new and interesting learning for you. At least it was for us. For example, see the date code? Tires have their DOB as well, and even unused, some mechanical changes happen to it as it gets “older”. For Frank, it means that a tire that is two years old, cannot be sold anymore.

Step 2. Indentification of suitable technologies

Identifying car tire was a challenge that might have seemed to be an easy one using the standard OCR (optical character recognition) approach. However, there are some other points to consider as well when choosing a technology path. With OCR, things to consider are as follows:

• There is no contrast between the symbols on the
  tire and the tire itself
• Some of the symbols are highly stylized and
  cannot be read but must be classified or identified
• Fonts used by some of the manufacturers are
  proprietary and there is no text body to train on
• Symbols which uniquely identify, for example, tire
  model are spread throughout the tire surface
• Difference between 2 unique tires might be in one
  letter which is 5mm big
• The speed which is required for conveyor belts is
  above 1.5 m/s to satisfy daily volume
• Tires come in various sizes (in our cases between
  12 and 22 inches)
• Tires have radial distortion on their symbols
• Due to high mass of the product (tire) and the
  industrial setting there are a lot of vibrations (e.g.
  heavy transport trucks and trains) going on which
  affect data acquisition process

Our clients also used RFID (radio frequency identification) before and problems that arose were:

• Time consuming Initial application of RFID chips on tires RFID chip increasing cost/tire ratio
• Tires are exposed to various forces during transport and handling and chips end up not working by the time they are read
• Speed of the conveyor belt dictates the distance between tires to prevent wrong readings from RFID (using RFID equipment which does not increase cost/ tire by too much)
• Other approaches required changes by manufacturers in the manufacturing process or middleman to invest serious money into tire labeling solution (color coding, QR embossing/printing etc.)

Thing to consider - Steps to take

Choosing the right hardware

The most important part (as with all AI/computer vision projects) is to make sure that data that is being analyzed is good data. Choosing the right camera for the job is essential. Making sure that industrial camera can handle all the concerns stated above:

• Depth of field must accommodate various tire sizes
• Aperture/shutter speed/ISO settings can be set to match the conveyor belt speed and eliminate motion blur without incurring noise
• Hardware can be procured quickly and in sufficient amounts • It has a nice API around which a solution can be built
• Build a nice Black Box which eliminates unpredictable lightning conditions, vibrations and floating particles while providing housing for camera and artificial lights and provides hardware interface to camera itself
• With the specified parameters it is imperative to use many sources of highfrequency lighting to provide enough stable light during short camera exposure time

Scouting the location

Implementation location might have some real-world problems which require you to include them in the problem-solving equation. A few examples:

• Initial tire offloading might be done at a place which is not covered by the security making it harder to leave the expensive equipment overnight. Therefore, the Black Box should be portable or easy to assemble. To prevent further errors there should be a calibration procedure to setup the camera and lighting after each move/assembly
• Networking infrastructure might be nonexistent or in bad shape
• The site might be remote making it hard to perform onsite maintenance therefore the solution should be as robust as possible
• The volume of the tires moved through the plant might not justify high-end hardware costs. Therefore, some thought should be given about algorithmic improvements to further reduce need for super expensive cameras

Meeting the personnel

As is the case with many automatization tools, manual laborers might perceive implementation of the new system as a threat to their jobs. It is of vital importance to have full support of the upper management and key workers to be able to run the training process with confidence that it will be followed through diligently.
Workers might not be too enthusiastic about following instructions during data collection phase since it burdens them with additional tasks and no perceived benefits. Communication during the project is the extremely important since data acquisition might be an arduous process and prone to human error. Solution for tire storing and distribution that uses modern technology of machine learning was a specific project we were working on. The extraction of the data from the tire requires the identification of suitable technologies, not just standard optical character recognition. To be able to describe the computer vision solution we developed, the taken approach can be divided into three main steps.