  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.
