Extended Abstract Version: CNN-based Human Detection System for UAVs in Search and Rescue
This is an incremental improvement for search and rescue operations using UAVs.
The paper tackled human detection in search and rescue using UAVs by implementing a CNN-based system on a Raspberry Pi with a quadcopter platform, achieving a processing speed of 3 fps.
This paper proposes an approach for the task of searching and detecting human using a convolutional neural network and a Quadcopter hardware platform. A pre-trained CNN model is applied to a Raspberry Pi B and a single camera is equipped at the bottom of the Quadcopter. The Quadcopter uses accelerometer-gyroscope sensor and ultrasonic sensor for balancing control. However, these sensors are susceptible to noise caused by the driving forces such as the vibration of the motors, thus, noise processing is implemented. Experiments proved that the system works well on the Raspberry Pi B with a processing speed of 3 fps.