Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Last update: Dec 31, 2022

Overview

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.

Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Related tags

Overview

Drone Detection using Thermal Signature

Requirements

Model Architecture

Training and Testing

Examples of predictions

Owner

Chong Yu Quan

Brain tumor detection using Convolution-Neural Network (CNN)

Hunt down social media accounts by username across social networks

Application of the L2HMC algorithm to simulations in lattice QCD.

A curated list of awesome Deep Learning tutorials, projects and communities.

Deep generative models of 3D grids for structure-based drug discovery

Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments for IV 2022.

Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

This repository is for Competition for ML_data class

Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences"

JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

UniFormer - official implementation of UniFormer

MQBench Quantization Aware Training with PyTorch

Storage-optimizer - Identify potintial optimizations on the cloud storage accounts

Tzer: TVM Implementation of "Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation (OOPSLA'22)“.