Drone detection using YOLOv5

Tushar Sarkar

Last update: Dec 20, 2022

Related tags

Deep Learning Detect_Drone

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.

Results:

Developed with ❤️ by Tushar Sarkar

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Comments

Torch error

PS D:\refcode\Detect_Drone> python detect.py --source ..\drone_1.MP4 detect: weights=yolov5s.pt, source=..\drone_1.MP4, imgsz=640, conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False YOLOv5 2021-6-30 torch 1.10.0+cpu CPU

Traceback (most recent call last): File "D:\refcode\Detect_Drone\detect.py", line 222, in main(opt) File "D:\refcode\Detect_Drone\detect.py", line 217, in main run(**vars(opt)) File "C:\Python39\lib\site-packages\torch\autograd\grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs) File "D:\refcode\Detect_Drone\detect.py", line 66, in run model = attempt_load(weights, map_location=device) # load FP32 model File "D:\refcode\Detect_Drone\models\experimental.py", line 120, in attempt_load ckpt = torch.load(w, map_location=map_location) # load File "C:\Python39\lib\site-packages\torch\serialization.py", line 607, in load return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "C:\Python39\lib\site-packages\torch\serialization.py", line 882, in _load result = unpickler.load() File "C:\Python39\lib\site-packages\torch\serialization.py", line 875, in find_class return super().find_class(mod_name, name) AttributeError: Can't get attribute 'SPPF' on <module 'models.common' from 'D:\refcode\Detect_Drone\models\common.py'>

opened by tuantdang 1