Paper Info
Title
A Fast and Accurate Edge Detection Algorithm for Real-Time Deep-Space Autonomous Optical Navigation
Abstract
This paper presents a fast and accurate edge detection algorithm for real-time autonomous optical navigation used in deep-space missions. The proposed algorithm optimizes the non-maximum suppression (NMS) mechanism and the adaptive threshold selection approach of the conventional Canny algorithm. Instead of computing gradient directions, the proposed NMS approach adopts the vertical and horizontal gradients to determine the diagonal directions of gradient directions. In addition, an optimized noise edge suppression mechanism is presented for getting thinner edges without sacrificing the performance in terms of computation complexity. Furthermore, unlike the conventional double-thresholding method, this paper proposes a single-threshold selection approach, thus reducing the computational complexity and easing the real-time embedded implementation. More importantly, the proposed single-threshold scheme can efficiently suppress the noise edges caused by craters and atmosphere covered on celestial bodies. Experimental results show that, compared with the traditional Canny edge detector, the proposed algorithm enables more accurate celestial body edge detection, while reducing a lot of computation complexity.
Year
DOI
Venue
2019
10.1109/IDAACS.2019.8924336
2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Keywords
Field
DocType
Canny edge detector,autonomous optical navigation,star centroid estimation,real-time processing
Diagonal,Canny edge detector,Computer vision,Celestial body,Computer science,Edge detection,Optical navigation,Artificial intelligence,NASA Deep Space Network,Computation complexity,Computational complexity theory
Conference
Volume
ISBN
Citations 
2
978-1-7281-4070-4
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hao Xiao1142.43
Yanming Fan200.34
Zhang Zhang358.09
Xin Cheng417.17