An automobile manufacturer is automating the placement of certain components on the bumpers of a limited-edition line of sports cars. The components are color coordinated, so the robots need to know the color of each car in order to select the appropriate bumper component. Models come in only four colors: blue, green, red, and white. You are hired to propose a solution based on imaging. How would you solve the problem of automatically determining the color of each car, keeping in mind that cost is the most important consideration in your choice of components?
The Correct Answer and Explanation is :
To solve the problem of automatically determining the color of each car, I would propose using a simple imaging system based on a color sensor, such as a camera with a machine vision algorithm. The solution should focus on cost-effectiveness while still ensuring reliable detection of the four car colors: blue, green, red, and white. Here’s the proposed solution:
Proposed Solution:
- Camera Setup: Use a low-cost RGB camera (or a basic color sensor) that captures the car’s color in real-time as it moves through the production line. The camera would be mounted at a fixed position to capture a clear image of the bumper.
- Image Processing: Utilize a basic image processing algorithm (such as color segmentation) that analyzes the pixel data of the image. The algorithm would extract the dominant color in the image by comparing the RGB values of the pixels to predetermined thresholds for each of the four colors (blue, green, red, and white).
- Color Detection Algorithm: A basic color classification system can be built using the RGB values. Since the four colors are distinct and easily distinguishable in the RGB color space, a simple algorithm can be used to check which of the four color thresholds the detected values fall into:
- Blue: Predominantly high values in the blue channel.
- Green: Predominantly high values in the green channel.
- Red: Predominantly high values in the red channel.
- White: All RGB channels are relatively high and close to each other (with values close to 255).
- Automation Integration: Once the color is identified, the robot can be triggered to select the appropriate bumper component based on the color detected.
Why This Works:
- Cost-Effective: RGB cameras are inexpensive and commonly available. Basic image processing algorithms don’t require high computational power and can run on affordable processors or microcontrollers.
- Simplicity: The approach does not require complex hardware or sensors, keeping the system simple and inexpensive.
- Accuracy: The four colors are distinct enough in the RGB space to be reliably detected with simple algorithms.
This solution balances cost with effectiveness by using standard, low-cost equipment and simple processing techniques to achieve reliable color detection for the automated process.