CNN based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation

TL;DR

This paper presents an AI-driven streetlight management system using CCTV and semantic segmentation to automatically control lighting based on pedestrian and vehicle presence, reducing energy waste.

Contribution

It introduces a novel automated streetlight control method combining computer vision and semantic segmentation, improving energy efficiency and operational accuracy.

Findings

The system accurately detects pedestrians and vehicles for lighting control.

It effectively distinguishes day and night conditions for automation.

The proposed model is economical and energy-efficient.

Abstract

One of the most neglected sources of energy loss is streetlights which generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of the operation, streetlights are frequently seen being turned ON during the day and OFF in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight 'ON' and 'OFF' to save energy consumption costs. According to the aforementioned approach, geolocation sensor data could be utilized to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. The validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting, and more resilient than conventional alternatives.