Deep Learning Lighting Control for Mixed-Color Scenes
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Solution Overview
Problem
Existing intelligent lighting systems struggle to provide optimal lighting adjustments, especially for mixed-color objects and environments, due to limitations in color recognition technology and user accessibility.
Innovation Solution
The implementation of a deep learning-based intelligent lighting control method that acquires scene images, processes them using a trained deep learning recognition model to extract scene and object information, and matches this information with a lighting recipe to adjust LED lighting accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color recognition algorithms are used to adjust lighting, then single-color objects can be recognized well, but mixed-color objects cannot be handled effectively
Solution Approach 1:
The patent introduces a camera as an intermediary device to capture scene images, which are then processed by a deep learning recognition model. This intermediary approach allows the system to analyze complex mixed-color scenes by converting visual information into processable data, overcoming the limitations of traditional direct color recognition algorithms
Solution Approach 2:
The patent replaces traditional color recognition algorithms with a deep learning recognition model. This substitution transitions from rule-based mechanical processing to intelligent algorithmic processing, enabling the system to handle mixed-color objects by learning from training datasets that include various color combinations and lighting scenarios
2Reliability
If deep learning technology is applied to lighting control, then ideal lighting adjustment can be achieved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning recognition model with extensive training datasets before deployment. The model is trained offline to recognize various objects, colors, and lighting scenarios, so that during actual lighting control, the system can directly use the pre-learned knowledge without requiring complex real-time processing
Solution Approach 2:
The system implements self-service through automated lighting adjustment based on deep learning recognition. Once the model is trained, it autonomously analyzes scene images, identifies objects and their color characteristics, and adjusts lighting parameters without human intervention, reducing the need for complex user interfaces and control mechanisms
3Extent of automation
If sensor-based color recognition is used, then automatic lighting adjustment is achieved, but background information is not considered
Solution Approach 1:
The patent enhances the functionality of the recognition system by using a deep learning model that can simultaneously identify multiple elements in a scene: foreground objects, background environments, and their color relationships. This multi-functional approach allows the system to consider background information alongside object information when making lighting adjustments
Solution Approach 2:
The patent transitions from one-dimensional object color recognition to multi-dimensional scene analysis. The deep learning model processes images to extract information about objects, backgrounds, lighting conditions, and spatial relationships, adding multiple dimensions of information processing that enable comprehensive lighting control considering all scene elements
Data Source
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AI summary
The present application disclose an intelligent lighting control method, including: acquiring a scene image with target object; processing the scene image by using a deep learning recognition model to obtain scene and object information; and matching the lighting recipe and sending it to a dimming unit to control an LED module to adjust the light. The present application, based on deep learning algorithm, adopts a recognition model trained by collecting images of lighting application scenes including target objects as deep learning datasets for deep learning training, validation and testing to process the images of the scenes, so as to obtain scene and object information and match a lighting recipe from a lighting recipe library. The powerful learning capability of deep learning is able to infer from the whole scene image to give an appropriate lighting adjustment solution, which in turn achieves the most ideal lighting adjustment needs.