Yolov7_Train

## Overview

Yolov7_Train is a dataset for object detection tasks - it contains Cars annotations for 1,256 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This is part of the dataset concerning the YOLO training of experimental images (Barchan dunes). In this dataset, you find the "YOLOv8 train" folder that contains the structure and images observed during lab experiments to train a CNN with images of barchan dunes, the "Train Results" folder that contains the figures and weights of YOLO detection of barchan dunes, the "Detection results" that contains some barchan dune detection and movies, and the "Code Files" that contains the scripts to detect barchan dunes, plot the morphology, train a YOLOv8, convert masks to polygons and plot resulting YOLO parameters. Some images are from Assis, W.R. and Franklin, E.M., Geophysical Research Letters, 47, e2020GL089464, 2020.

Custom Multi-Altitude Aerial Vehicles Dataset:

Created for publishing results for ICUAS 2023 paper "How High can you Detect? Improved accuracy and efficiency at varying altitudes for Aerial Vehicle Detection", following the abstract of the paper.

Abstract—Object detection in aerial images is a challenging task mainly because of two factors, the objects of interest being really small, e.g. people or vehicles, making them indistinguishable from the background; and the features of objects being quite different at various altitudes. Especially, when utilizing Unmanned Aerial Vehicles (UAVs) to capture footage, the need for increased altitude to capture a larger field of view is quite high. In this paper, we investigate how to find the best solution for detecting vehicles in various altitudes, while utilizing a single CNN model. The conditions for choosing the best solution are the following; higher accuracy for most of the altitudes and real-time processing ( > 20 Frames per second (FPS) ) on an Nvidia Jetson Xavier NX embedded device. We collected footage of moving vehicles from altitudes of 50-500 meters with a 50-meter interval, including a roundabout and rooftop objects as noise for high altitude challenges. Then, a YoloV7 model was trained on each dataset of each altitude along with a dataset including all the images from all the altitudes. Finally, by conducting several training and evaluation experiments and image resizes we have chosen the best method of training objects on multiple altitudes to be the mixup dataset with all the altitudes, trained on a higher image size resolution, and then performing the detection using a smaller image resize to reduce the inference performance. The main results

The creation of a custom dataset was necessary for altitude evaluation as no other datasets were available. To fulfill the requirements, the footage was captured using a small UAV hovering above a roundabout near the University of Cyprus campus, where several structures and buildings with solar panels and water tanks were visible at varying altitudes. The data were captured during a sunny day, ensuring bright and shadowless images. Images were extracted from the footage, and all data were annotated with a single class labeled as 'Car'. The dataset covered altitudes ranging from 50 to 500 meters with a 50-meter step, and all images were kept at their original high resolution of 3840x2160, presenting challenges for object detection. The data were split into 3 sets for training, validation, and testing, with the number of vehicles increasing as altitude increased, which was expected due to the larger field of view of the camera. Each folder consists of an aerial vehicle dataset captured at the corresponding altitude. For each altitude, the dataset annotations are generated in YOLO, COCO, and VOC formats. The dataset consists of the following images and detection objects:

Data	Subset	Images	Cars
50m	Train	130	269
50m	Test	32	66
50m	Valid	33	73
100m	Train	246	937
100m	Test	61	226
100m	Valid	62	250
150m	Train	244	1691
150m	Test	61	453
150m	Valid	61	426
200m	Train	246	1753
200m	Test	61	445
200m	Valid	62	424
250m	Train	245	3326
250m	Test	61	821
250m	Valid	61	823
300m	Train	246	6250
300m	Test	61	1553
300m	Valid	62	1585
350m	Train	246	10741
350m	Test	61	2591
350m	Valid	62	2687
400m	Train	245	20072
400m	Test	61	4974
400m	Valid	61	4924
450m	Train	246	31794
450m	Test	61	7887
450m	Valid	61	7880
500m	Train	270	49782
500m	Test	67	12426
500m	Valid	68	12541
mix_alt	Train	2364	126615
mix_alt	Test	587	31442
mix_alt	Valid	593	31613

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).