Best Image for Automatic License Plate Recognition – ALPR – part 1 Sharpness

Sharpness of the image is one important component that influences the accuracy of ALPR systems  Especially in high speed ALPR systems  such as open road tolling, it can be a challenge to get the required sharpness.

Here are some factors that impact sharpness and how to overcome them:

Depth of Field

A general definition of Depth of Field (DOF) is the distance between the nearest and farthest objects in a scene that appear acceptably sharp in an image.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.  Larger DOF with smaller opening

With an image for ALPR, the entire image needs to be sharp so a very large depth of field is required.

A larger DOF is achieved with smaller iris openings versus larger openings.  Two ways to allow for smaller iris openings is with a lower F number of the lens and with a more sensitive sensor.

Motion Blur

Motion blur is the fuzzy details that can appear when capturing a still image of a fast moving object, such as a car/license plate on the highway.  Again the example we showed in our last blog post:

Figure 2.  Insufficient sharpness due to motion blur

Again a lower F value of the lens can help here as it allows for shorter exposure times to better freeze the moving object.  More sensitive sensors also mean less light is required to get a good image, therefore also enabling shorter exposure times.

 

Lighting

Different license plates have different reflection coefficients.  For optimal results, the wavelength of the IR lighting should be matched to the license plate.

 

Iris Control

Having a fixed iris verses auto iris offers more control over the image.  By taking multiple images of the same object with different exposure times with a fixed iris better control over the focus and exposure is achieved.  Auto iris functionality can generate a dynamic depth of field and therefore unsharp portions in the image.

 

Conclusion

Since automatic license plate recognition (ALPR) or automatic number plate recognition (ANPR) relies on optical character recognition (OCR) of images, it makes sense that a higher quality input image results in higher accuracy. 

Sharpness is one component of image quality. It indicates the clarity of an image and therefore the amount of fine details in the image.  If all of the components in the vision system are not well matched and aligned, the spatial details will be blurred. If you match these well the total accuracy of your ALPR system can be increased

 

Next up we will discuss optimizing the contrast of an image for ALPR…