Contrasting YOLOv5, Transformer, and EfficientDet Detectors for Crop Circle Detection in Desert

Ongoing discoveries of water reserves have fostered an increasing adoption of crop circles in the desert in several countries. Automatically quantifying and surveying the layout of crop circles in remote areas can be of great use for stakeholders in managing the expansion of the farming land. This l...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Mekhalfi, Mohamed Lamine, Nicolo, Carlo, Bazi, Yakoub, Rahhal, Mohamad Mahmoud Al, Alsharif, Norah A., Maghayreh, Eslam Al
Format: Article
Language:English
Subjects:
Aerial imagery
Agriculture
Computer science
crop circles
Crops
Deep learning
Deserts
Detection
detection transformers (DETRs)
Detectors
Domains
efficientDet
Indexes
Internet
Object detection
precision farming
Surveying
Training
Transformers
YOLOv5
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Summary:Ongoing discoveries of water reserves have fostered an increasing adoption of crop circles in the desert in several countries. Automatically quantifying and surveying the layout of crop circles in remote areas can be of great use for stakeholders in managing the expansion of the farming land. This letter compares latest deep learning models for crop circle detection and counting, namely Detection Transformers, EfficientDet and YOLOv5 are evaluated. To this end, we build two datasets, via Google Earth Pro, corresponding to two large crop circle hot spots in Egypt and Saudi Arabia. The images were drawn at an altitude of 20 km above the targets. The models are assessed in within-domain and cross-domain scenarios, and yielded plausible detection potential and inference response.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3085139