An automobile manufacturer is automating the placement of certain components on the bumpers of a limited-edition line of sports cars. The components are colour coordinated, so the robots need to know the colour of each car in order to select the appropriate bumper component. Models come in only four colours: 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 colour of each car, keeping in mind that cost is the most important consideration in your choice of components?
The Correct Answer and Explanation is :
The most cost-effective solution for automatically determining the colour of each car would be to use a simple colour recognition system based on a camera and basic image processing software. Here’s how you could approach it:
Solution Overview:
- Camera Selection: Use a basic RGB (Red, Green, Blue) camera. These cameras are widely available and inexpensive, providing enough information for the task. The camera would capture an image of the car, focusing on its front bumper or a clearly visible section to ensure accurate colour detection.
- Image Processing: Once the image is captured, the next step is to process it. Using an algorithm, the camera system can analyze the predominant colour in the image. This could be done by splitting the image into RGB channels and calculating the average intensity of red, green, and blue across the area of interest.
- Colour Detection Algorithm:
- Calculate the average value of each RGB channel (Red, Green, Blue).
- Based on these averages, determine the dominant colour. For instance:
- If the average red value is higher than both green and blue, classify the car as red.
- If the green value is higher, classify it as green, and so on.
- The system could have predefined thresholds for classification (e.g., if the red value is significantly higher than green and blue, then the car is red).
- Cost Efficiency: The total cost of this solution remains low due to the simplicity of the hardware (just a basic RGB camera) and the image processing techniques (which don’t require expensive machine learning models). The software can be implemented using open-source libraries such as OpenCV, which is free and widely used for image processing tasks.
Conclusion:
This solution is both simple and inexpensive, meeting the primary cost consideration. By using an RGB camera and straightforward colour classification algorithms, the system would efficiently determine the car’s colour, allowing the robots to select the correct bumper components.