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 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.

Automated summary

The ongoing discoveries of water reserves have led to the increasing use of crop circles in desert areas in several countries. This study compares the effectiveness of three deep learning models, Detection Transformers, EfficientDet, and YOLOv5, for detecting and counting crop circles in remote desert regions.