Drone Detection using Thermal Signature
This repository highlights the work for night-time drone detection using a using an Optris PI Lightweight thermal camera. The work is published in the International Conference of Unmanned Air Systems 2021 (ICUAS 2021) and the paper can be read in detail in ICUAS_2021_paper.
Requirements
The following are the requirements with Python 3.7.7
tensorflow==2.4.0
opencv_contrib_python==4.5.1.48
numpy==1.20.3
Model Architecture
The following diagram highlights the architecture of model based on YOLOV3. However, unlike typical single image object detection, the model takes in the concatenation of a specified number of images in the past relative to the image of interest. This is to encapsulate the motion of the drone as an input feature for detection, a necessity given that thermal signatures of different are generally globular in shape after a certain distance depending on the fidelity of the thermal camera used. Further details can be found in ICUAS_2021_paper.
Training and Testing
Clone the repository, adjust the training/testing parameters in train.py as shown and execute the code. The training data comprises of data from a controlled indoor environment while the test data contains a mixture data from indoor and outdoor environments.
# Train options
TRAIN_SAVE_BEST_ONLY = True # saves only best model according validation loss (True recommended)
TRAIN_CLASSES = "thermographic_data/classes.txt"
TRAIN_NUM_OF_CLASSES = len(read_class_names(TRAIN_CLASSES))
TRAIN_MODEL_NAME = "model_2"
TRAIN_ANNOT_PATH = "thermographic_data/train"
TRAIN_LOGDIR = "log" + '/' + TRAIN_MODEL_NAME
TRAIN_CHECKPOINTS_FOLDER = "checkpoints" + '/' + TRAIN_MODEL_NAME
TRAIN_BATCH_SIZE = 4
TRAIN_INPUT_SIZE = 416
TRAIN_FROM_CHECKPOINT = False # "checkpoints/yolov3_custom"
TRAIN_LR_INIT = 1e-4
TRAIN_LR_END = 1e-6
TRAIN_WARMUP_EPOCHS = 1
TRAIN_EPOCHS = 10
TRAIN_DECAY = 0.8
TRAIN_DECAY_STEPS = 50.0
# TEST options
TEST_ANNOT_PATH = "thermographic_data/validate"
TEST_BATCH_SIZE = 4
TEST_INPUT_SIZE = 416
TEST_SCORE_THRESHOLD = 0.3
TEST_IOU_THRESHOLD = 0.45
Once the model is trained, you can test the model's predictions on images using detect_image.py. Adjust the the following parameters in detect_image.py and execute the code.
CLASSES = "thermographic_data/classes.txt"
NUM_OF_CLASSES = len(read_class_names(CLASSES))
MODEL_NAME = "model_2"
CHECKPOINTS_FOLDER = "checkpoints" + "/" + MODEL_NAME
ANNOT_PATH = "thermographic_data/test/images/pr"
OUTPUT_PATH = 'predicted_images/' + MODEL_NAME + "/pr"
DETECT_BATCH = False
DETECT_WHOLE_VID = True
BATCH_SIZE = 1804
IMAGE_PATH = ANNOT_PATH + "/free_3/free_3_frame_100"
INPUT_SIZE = 416
SCORE_THRESHOLD = 0.8
IOU_THRESHOLD = 0.45
Similarly, you can test the model's predictions on videos using detect_video.py. Adjust the following parameters in detect_video.py and execute the code.
CLASSES = "thermographic_data/classes.txt"
NUM_OF_CLASSES = len(read_class_names(CLASSES))
MODEL_NAME = "model_2"
CHECKPOINTS_FOLDER = "checkpoints" + "/" + MODEL_NAME
ANNOT_PATH = "raw_videos/free_2.mp4"
OUTPUT_PATH = 'predicted_videos/' + MODEL_NAME
INPUT_SIZE = 416
SCORE_THRESHOLD = 0.8
IOU_THRESHOLD = 0.45
Examples of predictions
An example of correct drone detection in indoor environment shown below.
An example of correct drone detection in outdoor environment shown below.
Video of model predictions shown in indoor environment can be found here.
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