Machine Vision Algorithm for Indoor Environment Perception and Intelligent Lighting Design

Under the strong promotion of global "carbon neutrality" policies, the issue of energy conservation and emission reduction has attracted attention from all sectors of society, especially in the field of public lighting. The energy waste phenomena of traditional systems, such as lighting not turned off when there are no people and excessive lighting when there are few people, are particularly prominent, which not only increases energy consumption costs but also adds to environmental pressure. To address this challenge, combined with the rapid development of machine vision (MV) and deep learning (DL) technologies, image recognition technology has opened up a new path for the intelligent transformation of lighting control systems. This article proposes an innovative intelligent lighting system based on MV algorithm, which can capture and analyze indoor environmental information in real time. With the help of cutting-edge image recognition technology, the system can accurately determine the presence, quantity, and activity area of indoor personnel, and then intelligently adjust the brightness and switch of the lighting system to achieve precise lighting and greatly improve energy efficiency. Experimental verification shows that the system exhibits excellent energy-saving performance in both simulated and actual scenarios, significantly reducing energy consumption and optimizing user experience, providing solid technical support for building a green and intelligent urban lighting system.