Differences Between Visual Color Recognition and Sensor Color Recognition

As technology advances, demand for color recognition grows. With AI and machine vision, we can recognize colors using computers and sensors. Here, Atonm discusses differences and use cases between visual color recognition and sensor-based color recognition.

1. Visual color recognition

Visual color recognition uses image processing. A camera captures images which are processed by a computer to extract color information. It requires handling lighting, noise, and environment, and commonly uses HSV, RGB, and LAB color models.

2. Sensor-based color recognition

Sensor-based color recognition uses color sensors composed of a photodetector and optical filters to measure specific color bands and output digital signals. Unlike visual methods, sensors do not require pixel-level image processing, offering faster response and lower latency.

Applications:

Visual recognition: object identification, defect detection, and robot vision.

Sensor recognition: automated control, light-source control, and IoT color data collection.

Comparison:

Accuracy: sensors often offer higher accuracy for specific color detection; visual systems may be affected by lighting and environment.

Speed: sensors are faster due to simpler processing.

Scope: visual recognition supports complex scenes; sensors are suitable for targeted color detection.

Cost: visual systems need cameras and computing resources; sensors are typically lower cost.