Authors :
Hosea Gian Kaunang; Andani Achmad; Supriadi Sahibu
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/yucwxjjx
Scribd :
http://tinyurl.com/yc3eat23
DOI :
https://doi.org/10.5281/zenodo.10443824
Abstract :
Smart control systems are broadly
implemented by means of controlling remotely or
automatically, however approaches the usage of
imagery are nevertheless not often discovered. The goal
of this research is to integrate a smarthome with a
control platform that controls lights using images as a
source of automated control. As input, images are
obtained and the Haar-cascade classification algorithm
is applied to calculate the number of human objects
within the room. however, a light sensor is also used as a
parameter in acquiring the light value in the room. If
there's a lack of value due to the presence of a human
object sitting in a chair, the system will provide extra
lighting and regulate the room lighting value to the
suitable room light standard. The control and settings
interface is made in web form, where several types of
control are available. communication between devices
inside the system uses the MQTT protocol. From the
research outcomes, this control system platform is
successful in running and controlling lamp lighting
based on image detection input. There are differences in
the level of detection accuracy that is influenced by the
distance between the camera and the object, apart from
that the quality of the camera sensor in capturing
images additionally affects detection overall
performance. At the same time, objects tend to be easily
detected, are objects that do not use facial accessories
such as mask, glasses and so on. It was additionally
found that the average reduction in light value inside
the room became 0.83 lux for each 2-3 humans sitting in
the room. This research answers a control system
solution for a smart home with an indoor image
detection approach. similarly research is needed to
precisely calculate the comparison of the accuracy of
object detection with other image detection algorithms.
Keywords :
Control System, OpenCV, MQTT.
Smart control systems are broadly
implemented by means of controlling remotely or
automatically, however approaches the usage of
imagery are nevertheless not often discovered. The goal
of this research is to integrate a smarthome with a
control platform that controls lights using images as a
source of automated control. As input, images are
obtained and the Haar-cascade classification algorithm
is applied to calculate the number of human objects
within the room. however, a light sensor is also used as a
parameter in acquiring the light value in the room. If
there's a lack of value due to the presence of a human
object sitting in a chair, the system will provide extra
lighting and regulate the room lighting value to the
suitable room light standard. The control and settings
interface is made in web form, where several types of
control are available. communication between devices
inside the system uses the MQTT protocol. From the
research outcomes, this control system platform is
successful in running and controlling lamp lighting
based on image detection input. There are differences in
the level of detection accuracy that is influenced by the
distance between the camera and the object, apart from
that the quality of the camera sensor in capturing
images additionally affects detection overall
performance. At the same time, objects tend to be easily
detected, are objects that do not use facial accessories
such as mask, glasses and so on. It was additionally
found that the average reduction in light value inside
the room became 0.83 lux for each 2-3 humans sitting in
the room. This research answers a control system
solution for a smart home with an indoor image
detection approach. similarly research is needed to
precisely calculate the comparison of the accuracy of
object detection with other image detection algorithms.
Keywords :
Control System, OpenCV, MQTT.
