Detect drones from input image, video or real-time feed.

• Refer to requirement.txt for environment specifications.

• Download the pre-trained YOLOv3 weights from here.

• Download an image of a dog to test object detection.

  $ python yolo3_one_file_to_detect_them_all.py -w yolo3.weights -i dog.jpg 

• Download pretrained weights for backend from here. This weight must be put in the root folder of the repository.

YOLOv3 training requires images with .xml files in PASCAL-VOC format.

Click [here] to Download Drone Dataset with .xml files in PASCAL-VOC format.

Alternatively, if you want to create your own dataset, follow these steps:

1. Collect images from Kaggle Dataset or Google Images.
2. Download LabelImg(a graphical image annotation tool) from this GitHub Repo.
3. Setup LabelImg and draw a box around the object of interest in each image using the tool to generate XML files.
4. Place all your dataset images in the images folder and the xml files in the annots folder.

• Specify path of the images and annots folder in the "train_image_folder" and "train_annot_folder" fields.
• The "labels" setting lists the labels to be trained on. Only images, which has labels being listed, are fed to the network.

{ "model" : { "min_input_size": 288, "max_input_size": 448, "anchors": [17,18, 28,24, 36,34, 42,44, 56,51, 72,66, 90,95, 92,154, 139,281], "labels": ["drone"] }, "train": { "train_image_folder": "F:/Drone/Drone_mira_dataset/images/", "train_annot_folder": "F:/Drone/Drone_mira_dataset/annots/", "cache_name": "drone_train.pkl", "train_times": 8, # the no. of times to cycle through the training set "pretrained_weights": "", # specify path of pretrained weights,but it's fine to start from scratch  "batch_size": 2, # the no. of images to read in each batch "learning_rate": 1e-4, # the base learning rate of the default Adam rate scheduler "nb_epochs": 50, # no. of epoches "warmup_epochs": 3, "ignore_thresh": 0.5, "gpus": "0,1", "grid_scales": [1,1,1], "obj_scale": 5, "noobj_scale": 1, "xywh_scale": 1, "class_scale": 1, "tensorboard_dir": "logs", "saved_weights_name": "drone.h5", # name of model file to which our trained model is saved "debug": true # turn on/off the line to print current confidence,position,size,class losses,recall }, "valid": { "valid_image_folder": "C:/drone/valid_image_folder/", "valid_annot_folder": "C:/drone/valid_annot_folder/", "cache_name": "drone_valid.pkl", "valid_times": 1 } }

$ python gen_anchors.py -c config.json

Copy the generated anchors printed on the terminal to the anchors setting in config.json.

$ python train.py -c config.json

By the end of this process, the code will write the weights of the best model to file drone.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file). The training process stops when the loss on the validation set is not improved in 3 consecutive epoches.

$ python predict.py -c config.json -i /path/to/image/or/video/or/cam

• For an image use : $ python predict.py -c config.json -i test.jpg
• For a video use : $ python predict.py -c config.json -i test.mp4
• For a real-time feed use : $ python predict.py -c config.json -i webcam

It carries out detection on the image and write the image with detected bounding boxes to the output folder.

Compute the mAP performance of the model defined in saved_weights_name on the validation dataset defined in "valid_image_folder" and "valid_annot_folder"

$ python evaluate.py -c config.json

Demo:

• Download the sample output for drone detection in a video.