  The use of Unmanned Aerial Vehicles (UAVs) as a substitute for ordinary
vehicles in applications of search and rescue is being studied all over the
world due to its flexible mobility and less obstruction, including two main
tasks: search and rescue. This paper proposes an approach for the first task of
searching and detecting victims using a type of convolutional neural network
technique, the Single Shot Detector (SSD) model, with the Quadcopter hardware
platform, a type of UAVs. The model used in the research is a pre-trained model
and is applied to test on a Raspberry Pi model B, which is attached on a
Quadcopter, while a single camera is equipped at the bottom of the Quadcopter
to look from above for search and detection. The Quadcopter in this research is
a DIY hardware model that uses accelerometer and gyroscope sensors and
ultrasonic sensor as the essential components for balancing control, however,
these sensors are susceptible to noise caused by the driving forces on the
model, such as the vibration of the motors, therefore, the issues about the PID
controller, noise processing for the sensors are also mentioned in the paper.
Experimental results proved that the Quadcopter is able to stably flight and
the SSD model works well on the Raspberry Pi model B with a processing speed of
3 fps and produces the best detection results at the distance of 1 to 20 meters
to objects.
