Unlike what is shown on TV, you cannot zoom into a blurry image and expect to get more details. An image with acceptable sharpness and contrast must be acquired with the appropriate system from the start. This means the right image sensor, camera, optics, and lighting all combined in a reliable way.
So what defines good image quality for ALPR?
The first step is to have reliable triggering in order to have the license plate in the proper location in the image, which can be especially difficult in multi-lane systems. After that, a good/accurate image can be described by:
- Good Sharpness
- Sufficient Contrast
- Free of artifacts
- And sometimes with accurate color
These are qualitative explanations so here some images to demonstrate the point.
Figure 1. Artifacts from insufficient lighting control
Figure 2. Insufficient Sharpness due to Motion Blur
Figure 3. Insufficient Contrast from limited dynamic range
Some good background information on license plate acquisition algorithms and technology is provided on: http://www.platerecognition.info/1102.htm
The sources of these image quality issues can vary. Some possible reasons are shown in the table below and are further detailed in our next series of blogs.
Image Quality Parameter
Corresponding Source of Limitations
Image System Parameters to Control
Limited Depth of Field
F Value of Lens
Sensitivity of Image Sensor
Limited number of images
Reflections of the license plate
Reflections of snow, rain, flog
Frame rate of the image sensor/camera
Dynamic range of the image sensor/camera
Bright spots and streaks from sun exposure and reflections
Alignment of filter, lens, and lighting
Channel Matching in the camera
Blooming and smear control in the camera
Inaccurate Color reproduction
Accurate color calculations and automatic white balance in the camera
Proper alignment of the entire optical path determines the quality of the image captured, which is especially vital in high-speed situations. With a higher quality of the input image, the better starting point for the license plate recognition algorithm, and therefore the higher license plate recognition accuracy.
You can already read the following blogs about this:
- Opportunities and challenges of taking advantage of advancements in machine vision for ITS applications
- Camera and image system requirements for different automatic license plate recognition or automatic number plate recognition (ALPR or ANPR) applications.
- To learn more about accurate color reproduction, click here.