Amine Amri
Published © Apache-2.0

Drone Patrol

An autonomous drone equipped with an AI to detect fire on an early stage and deliver first-aid kits to victims.

IntermediateFull instructions providedOver 8 days5,224
Bonus Prizes
HoverGames Challenge 1: Fight Fire with Flyers

Things used in this project

Hardware components

NVIDIA Jetson Nano Developer Kit
NVIDIA Jetson Nano Developer Kit
×1
Camera Module
Raspberry Pi Camera Module
×1
SG90 Micro-servo motor
SG90 Micro-servo motor
×1
Wifi module
×1
RDDRONE-FMUK66
NXP RDDRONE-FMUK66
×1
KIT-HGDRONEK66
NXP KIT-HGDRONEK66
×1

Software apps and online services

OpenCV
OpenCV
MAVLink
PX4 MAVLink
TensorFlow
TensorFlow

Hand tools and fabrication machines

3D Printer (generic)
3D Printer (generic)

Story

Read more

Custom parts and enclosures

Top part of he Drop mechanism

Bottom part of he Drop mechanism

Piston to release the payload

Ring to attach to payload

Schematics

Payload release system

Code

Drone Patrol Python code (Desktop version)

Python
import cv2
import os
import sys
import math
import time

import tflearn
from tflearn.layers.core import *
from tflearn.layers.conv import *
from tflearn.layers.normalization import *
from tflearn.layers.estimator import regression

from dronekit import connect, VehicleMode, mavutil
from dronekit.mavlink import MAVConnection

def set_servo(servo_number, pwm_value):
    pwm_value_int = int(pwm_value)
    msg = vehicle.message_factory.command_long_encode(0,0,mavutil.mavlink.MAV_CMD_DO_SET_SERVO,0,servo_number, pwm_value_int,0,0,0,0,0)
    vehicle.send_mavlink(msg)

def my_new_fix_targets(message):
    pass

#return a CNN tensorflow model
def construct_fireCNN (x,y):

    network = tflearn.input_data(shape=[None, y, x, 3], dtype=tf.float32)

    network = conv_2d(network, 64, 5, strides=4, activation='relu')

    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)

    network = conv_2d(network, 128, 4, activation='relu')

    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)

    network = conv_2d(network, 256, 1, activation='relu')

    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)

    network = fully_connected(network, 4096, activation='tanh')
    network = fully_connected(network, 4096, activation='tanh')
    network = fully_connected(network, 2, activation='softmax')

    # constuct final model

    model = tflearn.DNN(network, checkpoint_path='fire',
                        max_checkpoints=1, tensorboard_verbose=2)

    return model



connection_string = "/dev/ttyUSB0" #connection to Jetson Nano USB port
baud_rate = 115200
print(">>>> Connecting with the UAV <<<")
vehicle = connect(connection_string,baud_rate, wait_ready=True)     #- wait_ready flag hold the program untill all the parameters are been read
udp_conn = MAVConnection('udpin:192.168.0.22:15667', source_system=1) #open an incoming udp connection on Jetson Nano
vehicle._handler.pipe(udp_conn)
udp_conn.master.mav.srcComponent = 1  # needed to make QGroundControl work!
udp_conn.start()
udp_conn.fix_targets = my_new_fix_targets







################################################################################

if __name__ == '__main__':

################################################################################
	

    while not vehicle.is_armable:
    	print("waiting to be armable")
    	time.sleep(1)

    print("Arming motors")
    vehicle.mode = VehicleMode("AUTO")
    vehicle.armed = True

    while not vehicle.armed: time.sleep(1)

    # construct and display model

    model = construct_fireCNN (224, 224)
    print("Constructed Fire CNN ...")

    model.load(os.path.join("model", "fire"),weights_only=True)
    print("Loaded CNN network weights ...")

################################################################################

    # network input sizes

    rows = 224
    cols = 224

    # display and loop settings

    windowName = "HoverGames Challenge 1: Fight Fire with Flyers with NXP";
    keepProcessing = True;

    # load video from default camera

    video = cv2.VideoCapture(0)
    print("Loaded video ...")

    # create window

    cv2.namedWindow(windowName, cv2.WINDOW_NORMAL);

    # get video properties

    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH));
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = video.get(cv2.CAP_PROP_FPS)
    frame_time = round(1000/fps);

    while (keepProcessing):

        # start a timer (to see how long processing and display takes)

        start_t = cv2.getTickCount();

        # get video frame from camera

        ret, frame = video.read()


        # re-size image to network input size and perform prediction

        small_frame = cv2.resize(frame, (rows, cols), cv2.INTER_AREA)
        output = model.predict([small_frame])

        # label image based on prediction

        if round(output[0][0]) == 1:
            cv2.rectangle(frame, (0,0), (width,height), (0,0,255), 50)
            cv2.putText(frame,'FIRE',(int(width/16),int(height/4)),
                cv2.FONT_HERSHEY_SIMPLEX, 2,(255,255,255),7,cv2.LINE_AA);
            msg = vehicle.message_factory.statustext_encode(1,b'Alert Fire at : ' + str( vehicle.location.global_frame).encode())
            udp_conn.master.mav.send(msg)
        else:
            cv2.rectangle(frame, (0,0), (width,height), (0,255,0), 50)
            cv2.putText(frame,'CLEAR',(int(width/16),int(height/4)),
                cv2.FONT_HERSHEY_SIMPLEX, 2,(255,255,255),7,cv2.LINE_AA);
            

        # stop the timer and convert to ms. (to see how long processing and display takes)

        stop_t = ((cv2.getTickCount() - start_t)/cv2.getTickFrequency()) * 1000;

        # image display and key handling

        cv2.imshow(windowName, frame);

        # wait fps time or less depending on processing time taken (e.g. 1000ms / 25 fps = 40 ms)

        key = cv2.waitKey(max(2, frame_time - int(math.ceil(stop_t)))) & 0xFF;
        if (key == ord('x')):
            keepProcessing = False;
        elif (key == ord('f')):
            cv2.setWindowProperty(windowName, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN);

Github

Credits

Amine Amri

Amine Amri

10 projects • 19 followers
I’m a financial risk engineer in Paris, France with a passion for computer science, electrical engineering, robotics and embedded systems.

Comments

Awards
Bonus Prizes
HoverGames Challenge 1: Fight Fire with Flyers
Related channels and tags
  • Drones
  • Machine Learning & AI
  • NVIDIA
  • NXP
  • OpenCV
  • PX4
  • Python on Hardware
  • Raspberry Pi
  • TensorFlow