BUSCADOR RECOLECTA

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. 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.

Las imágenes tienen formato JPEG, Email de contacto: sedanoj@unican.es, Imágenes del vuelo fotogramétrico con Drone de las instalaciones del Balneario La Hermida, Drone photogrammetric flight images of the Balneario La Hermida facilities