CNN (Convolution Neural Network) Based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation

Abstract

One of the most neglected sources of energy loss is streetlights that 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 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, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. 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.

Citation

Sourav, M.S.U., Wang, H., Chowdhury, M.R. and Bin Sulaiman, R. (2023), "CNN (Convolution Neural Network) Based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation", Dadwal, S.S., Jahankhani, H., Bowen, G. and Nawaz, I.Y. (Ed.) Technology and Talent Strategies for Sustainable Smart Cities, Emerald Publishing Limited, Leeds, pp. 229-246. https://doi.org/10.1108/978-1-83753-022-920231011