Front and Rear Park Assist Sensor Color Classification
Ensure installation of the correct color of FPA/RPA sensors with deep learning solutions
Graphical programming environment for deep learning-based industrial image analysis
Between four to 12 Front or Rear Park Assist (FPA/RPA) ultrasonic sensor assemblies are embedded in both bumpers of all new cars. For aesthetic reasons, these sensors are manufactured in a variety of shades to match bumper color. Modern automobile paint can contain a variety of light-scattering particles and metallic flecks, varies slightly in color from one spot to another, and is produced in a range of closely related shades.
Red, green, blue (RGB) or hue, saturation, intensity (HSI) values will change depending on angle and orientation. As a result, picking the correct sensor color from a wide color inventory to precisely match a specific bumper color is a difficult problem. Installation of the incorrect sensor can lead to rejection by the end user. Sensors must be quickly and accurately matched to bumper color so that workers can install the specified model.
Given all these possible variables and overlaps, rule-based machine vision struggles to make the correct sensor/paint matching decision. Where the colors come close to overlapping, the human eye also interprets color differently from person to person.
Cognex Deep Learning is trained on a range of different images, at different angles and rotations and the classification tool robustly categorizes the paint colors. Then, when making the choice, Cognex Deep Learning examines the image as a whole, properly weighting every variation, reflection, refraction, granularity, and shade within that image to make the best match.