Drone detection using YOLOv5

Last update: Dec 20, 2022

Related tags

Overview

This drone detection system uses YOLOv5 which is a family of object detection architectures and we have trained the model on Drone Dataset.

Overview

Install

Python >= 3.6.0 required with all requirements.txt dependencies installed:

$ git clone https://github.com/tusharsarkar3/Detect_Drone.git
$ pip install -r requirements.txt

Training

The structure of the file system is of great importance here so these images will show you the correct way of organizing it. The main folder named datasets should be on the same level as this repository. The next steps are elaborated in the images:

The two folders with images and labels respectively should be inside the dataset folder.

The images directory should contain the training images and the validation images respectively.

The labels directory should contain the training labels and the validation labels respectively.

Run commands below to reproduce results on Drone Dataset dataset..

$ $ python train.py --img 640 --batch 16 --epochs 15 --data coco128.yaml --weights yolov5s.pt

Check out YOLOv5 for more information.

Inference

$ python detect.py --weights 'path to the best set of weights' --source 0  # webcam       
                                                                        file.jpg  # image 
                                                                        file.mp4  # video
                                                                        path/  # directory
                                                                        path/*.jpg  # glob
                                                                        'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                                                                        'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

The results will be stored in a new directory named run which will be on the same level as the root directory.

Check out YOLOv5 for more information.

Drone detection using YOLOv5

Related tags

Overview

Overview

Results:

Developed with ❤️ by Tushar Sarkar

Owner

Tushar Sarkar

Reproducing Results from A Hybrid Approach to Targeting Social Assistance

Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Implementation for Shape from Polarization for Complex Scenes in the Wild

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

OpenDILab Multi-Agent Environment

MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios

AI Based Smart Exam Proctoring Package

Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI

基于Paddlepaddle复现yolov5，支持PaddleDetection接口

Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution'

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

End-to-End Referring Video Object Segmentation with Multimodal Transformers

Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

Seeing if I can put together an interactive version of 3b1b's Manim in Streamlit

Custom TensorFlow2 implementations of forward and backward computation of soft-DTW algorithm in batch mode.

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

Code for the paper: Hierarchical Reinforcement Learning With Timed Subgoals, published at NeurIPS 2021