Аннотация:
The edge detection performance of the HED and PiDiNet networks is compared on noised natural
images. To research the robustness of edge detection with HED and PiDiNet networks, the technique of
detecting the influence of the noise level on the CNN edge detector efficiency is proposed. At first, as a
result of literature review, HED and PiDiNet are selected to detect edges on noisy images. Then types
of image noise of interest to the researcher and the noise parameters which will be controlled by the
researcher are determined. We considered white Gaussian noise, impulse noise, and speckle noise.
Mathematical modeling of noisy images is performed. Next, the training and test sets from image
datasets for edge detection is selected. After that, the researched networks are learned to detect edges
on training set of images or weights of pre-trained networks are loaded. Further the measures of edge
detection performance are selected, specifically, Precision, Recall, F1-score, and Pratt's Figure of Merit.
Then images of test set are corrupted with controlled level of noise. The trained networks are applied
to noisy images and the edge detection performance is evaluated depending on the noise level in the
images. The obtained results are analyzed to generate the recommendations allowing to determine
which CNN is better for edge detection when natural images affected by different level of white
Gaussian noise, or impulse noise, or speckle noise. In particular, the HED network is generally
preferred at high noise levels. The PiDiNet network is best used when the noise level is low.
