1) Title:

Mosquitonet

2) Author(s):

Saúl Cano Ortiz

3) Contact:

- e-mail: saul.cano@unican.es
- adress: Av. de Los Castros, 44, 39005 Santander, Cantabria

4) Brief description:

Mosquitonet is an open-source pavement distress dataset collected with a low-cost vehicle-mounted system for pavement distress detection through Deep Learning algorithms.
There are 7099 images with 13 distress classes, annotated by experts in 12 formats: COCO, PASCAL VOC, YOLO/YOLOv4-v7 PyTorch, TF Object Detection, TFRecord, YOLOv3 Keras, Retinanet Keras and CreateML.

5) Data collection date:

From 01/06/2022 to 30/10/2022

6) Geographical location:

Cantabria (Santander, Spain)

7) Spatial coverage:

This dataset has been created to enable the worldwide scientific community to make progress in the area of pavement distress detection from images.

8) Collection methodology:

The images were gathered by means of a low-cost, high-resolution and fast-acquisition system, Mosquito. The Mosquito system is an unmanned aerial vehicle mounted on a 3D-printed 
structure that is attached to a vehicle using a suction cup system. The Mosquito system provides images and GPS coordinates per image for speeds up to 120 km/h, where its remote 
control allows the co-pilot to adjust its parameters in real time for better capture. Annotations for training Deep Learning models were performed manually by pavement experts. 

9) Format:

- The images are: .jpg
- The annotations are: COCO (.json), PASCAL VOC (.xml), YOLOvx (.txt), TF Object Detection (.csv), TFRecord (.tfrecord), YOLOv3 Keras (.txt), Retinanet keras (.csv), CreateML (.json).

10) Data structure

Each format contains around 2.48 GB, where train/validation/test split is 77/15/8. The structure for each dataset format is:

COCO (mapsia.v11i.coco.zip):
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.coco.json
    valid/
        1072 images (.jpg)
        _annotations.coco.json
    test/
        543 images (.jpg)
        _annotations.coco.json

PASCAL VOC (mapsia.v11i.coco.zip):
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        5484 annotations (.xml)
    valid/
        1072 images (.jpg)
        1072 annotations (.xml)
    test/
        543 images (.jpg)
        543 annotations (.xml)

YOLO PyTorch (mapsia.v11i.darknet.zip)
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        5484 annotations (.txt)
    valid/
        1072 images (.jpg)
        1072 annotations (.txt)
    test/
        543 images (.jpg)
        543 annotations (.txt)

YOLOv3 Keras (mapsia.v11i.yolokeras.zip)
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.txt
        _classes.txt
    valid/
        1072 images (.jpg)
        1072 annotations (.txt)
        _annotations.txt
        _classes.txt
    test/
        543 images (.jpg)
        543 annotations (.txt)
        _annotations.txt
        _classes.txt

YOLOv5 PyTorch(mapsia.v11i.yolov5pytorch.zip):
    README.dataset.txt
    README.roboflow.txt
    data.yaml
    images/
        train/
            5484 images (.jpg)
        valid/
            1072 images (.jpg)
        test/
            543 images (.jpg)
    labels/
        train/
            5484 annotations (.txt)
        valid/
            1072 annotations (.txt)
        test/
            543 annotations (.txt)

YOLOv6 PyTorch(mapsia.v11i.mt-yolov6.zip):
    README.dataset.txt
    README.roboflow.txt
    data.yaml
    images/
        train/
            5484 images (.jpg)
        valid/
            1072 images (.jpg)
        test/
            543 images (.jpg)
    labels/
        train/
            5484 annotations (.txt)
        valid/
            1072 annotations (.txt)
        test/
            543 annotations (.txt)

CreateML (mapsia.v11i.createml.zip):
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.createml.json
    valid/
        1072 images (.jpg)
        _annotations.createml.json
    test/
        543 images (.jpg)
        _annotations.createml.json


TFRecord (mapsia.v11i.tfrecord.zip):
    README.dataset.txt
    README.roboflow.txt
    train/
        distress.tfrecord
        distress.label_map.pbtxt
    valid/
        distress.tfrecord
        distress.label_map.pbtxt
    test/
        distress.tfrecord
        distress.label_map.pbtxt

TF Object Detection (mapsia.v11i.tensorflow.zip):
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.csv
    valid/
        1072 images (.jpg)
        _annotations.csv

    test/
        543 images (.jpg)
        _annotations.csv

YOLOv4 PyTorch (mapsia.v11i.yolov4pytorch.zip)
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.txt
        _classes.txt
    valid/
        1072 images (.jpg)
        1072 annotations (.txt)
        _annotations.txt
        _classes.txt
    test/
        543 images (.jpg)
        543 annotations (.txt)
        _annotations.txt
        _classes.txt

YOLOv7 Pytorch (mapsia.v11i.yolov7pytorch.zip):
    README.dataset.txt
    README.roboflow.txt
    data.yaml
    train/
        images/
            5484 images (.jpg)
        labels/
            5484 annotations (.txt)
    valid/
        images/
            1072 images (.jpg)
        labels/
            1072 annotations (.txt)
    test/
        images/
            543 images (.jpg)
        labels/
            543 annotations (.txt)

Retinanet keras (mapsia.v11i.retinanet.zip)
    README.dataset.txt
    README.roboflow.txt
    train/
        5484 images (.jpg)
        _annotations.csv
    valid/
        1072 images (.jpg)
        _annotations.csv

    test/
        543 images (.jpg)
        _annotations.csv
11) Abbreaviations

None

12) Requirements (software to visualize data)

None