Abstract
Machine vision calls for the use of detectors to ascertain the features and type of object portrayed in the image. The employment of unmanned aerial vehicles (UAVs), which can function freely in active and precarious settings, is currently gaining momentum. These vehicles are mainly used for the detecting, classifying and tracking of an object. However, the achievement of these objectives necessitates the involvement of an effective edge detection procedure. Sobel, Canny, Prewitt and LoG are among the many edge detection procedures presently available. In this endeavour, we opted for the utilization of UTeM UAVs images for an evaluation of these edge detection procedures. During our investigations, the ground truth edge images were corroborated by a specialist in this field. The results obtained from these investigations revealed that in terms of accuracy, precision, sensitivity and f-measure, the Prewitt procedure outperforms the other methods mentioned.
Highlights
When it comes to machine vision, the analysis of an image is significantly determined by the particulars related to its edges
Edge detection is an indispensable instrument for image processing, machine vision and computer vision
The average f-measure results acquired from all images in the Universiti Teknikal Malaysia Melaka (UTeM) unmanned aerial vehicles (UAVs) dataset revealed that the performance of the Prewitt method surpasses those of the other methods
Summary
When it comes to machine vision, the analysis of an image is significantly determined by the particulars related to its edges. The mathematical procedures related to edge detection are aimed at distinguishing the points in a digital image at which the luminosity of the image varies abruptly (or put differently, has discontinuities). Edge detection is an indispensable instrument for image processing, machine vision and computer vision This is especially so in matters related to the detection and extraction of features [1]. Studies conducted on the Canny technique [12], [13] have served to improve its performance in the context of iris, scenery, retina and handwritten images This endeavour forwards an innovative UAVs image dataset for the site of Universiti Teknikal Malaysia Melaka (UTeM). We discussed and evaluated the Sobel, LoG, Canny and Prewitt edge detection procedures
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