Change in Light Intensity and Automation of Street Lamp Through Machine Learning Algorithm Image Processing

Street lighting provides a number of important benefits. It can be used to promote security in urban areas and also improve safety for drivers, riders, and pedestrians. Over the past decade, there has been an increase in energy demand. As a result of this, halogen-based street lights were replaced by LED technology, which significantly reduced energy consumption. Since the LED light system was introduced in street lights, quite less improvement has been implemented. There are a lot of improvements that can be made to overcome manual operating, difficult-to-address faults, and to make the street light more energy efficient. The proposed model deploys Raspberry Pi and a microcontroller in street lights to make them automated and to conserve energy by adjusting the street light intensity to low when no movement is detected. Movement detection is done using a camera through image processing. Using OpenCV, Image processing is done. As this system uses a Raspberry Pi, every light can be connected to a network which helps in maintenance and also remote access can be taken to control the street lights, and faults can be addressed easily.