Object identification and surveillance based on deep learning algorithms for quadcopters

Abstract

Drone technology is evolving for the applications like surveillance, observation, rescue and control crimes, military, agriculture, civil and many more purposes. But these surveillance systems are monitored by human interaction so there may be some negligence and malfunction may happen due to lack of observation. The objective of the work was development of unmanned aerial vehicle and implementation of the deep learning, image processing tools in the quad-copter based surveillance system. Development of the quad-copter was done by using necessary components such as Arms and power distribution board with help of fasteners. And the brushless dc motors are fitted with electronic speed controllers. And the suitable propellers are fitted to the motors, from which thrust is obtained. The Arduino Uno microcontroller is used as flight controller with MPU6050. Sensor fusion concept coding was used for accelerometer and gyroscope for stable direction orientations of Aerial vehicle. Multiwii platform was used to build the flight controller for achieving desire rotation of motors as well as proper directions and speed. The receiver was installed in the quad-copter for wireless control with transmitter of 2.4 GHz range. And IP camera was used, from which the surveillance visuals are taken for monitoring. Battery of 2200mah capacity of 3 cells was used for power supply of whole system. The visuals were obtained in raspberry pi, the live video stream/images are processed with the deep learning tools i.e., Open CV, Tensor flow, yolo for effective surveillance.