A Fast and Efficient Method for Solving the Multiple Generalized Circle Detection Problem

2018 ◽  
Author(s):  
Rudolf Scitovski ◽  
Kristian Sabo
Keyword(s):  
Efficient Method ◽  
Circle Detection ◽  
Detection Problem ◽  
Generalized Circle

Fast Ellipse Detection Method Based on Coordinates Transformation

2014 ◽  
Vol 926-930 ◽  
pp. 3038-3041
Author(s):  
Cheng Wang
Keyword(s):  
Digital Image ◽  
Detection Method ◽  
Closed Curve ◽  
New Method ◽  
Detection Methods ◽  
Circle Detection ◽  
Detection Problem ◽  
Ellipse Detection ◽  
Coordinates Transformation ◽  
Detection Speed

In this paper, we introduce a new method for ellipse detection. For any object has closed curve in a digital image, it is easy to calculate the centroid of the object. We assume the object is an ellipse, and then by rotating, scaling this object, it can be transformed to a circle. So, ellipse detection problem becomes circle detection problem. Compared with other detection methods, our method only need process border points of the object, hence has higher detection speed.

scholarly journals Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3

2021 ◽  
Vol 11 (5) ◽  
pp. 2238
Author(s):  
Mohamed Lamine Mekhalfi ◽  
Carlo Nicolò ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Eslam Al Maghayreh
Keyword(s):  
Large Scale ◽  
Google Earth ◽  
Western Desert ◽  
Desert Area ◽  
Circle Detection ◽  
Detection Problem ◽  
Training Set ◽  
Detection Rates ◽  
Learning Framework ◽  
Detection And Counting

Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. The dataset totals 2511 crop circle samples. With a small training set and a relatively large test set, plausible detection rates were obtained, scoring a precision of 1 and a recall of about 0.82 for Mask R-CNN and a precision of 0.88 and a recall of 0.94 regarding YOLOv3.

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