Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

To maintain the reliability, availability, and sustainability of electricity supply, electricity
companies regularly perform visual inspections on their transmission and distribution …

Abstract Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level
performance in edge detection with the rich and abstract edge representation capacities …

Convolutional neural networks have made significant progresses in edge detection by
progressively exploring the context and semantic features. However, local details are …

Cracks are typical line structures that are of interest in many computer-vision applications. In
practice, many cracks, eg, pavement cracks, show poor continuity and low contrast, which …

We explore the potential of pooling techniques on the task of salient object detection by
expanding its role in convolutional neural networks. In general, two pooling-based modules …

Object detection generally requires sliding-window classifiers in tradition or anchor-based
predictions in modern deep learning approaches. However, either of these approaches …

This paper proposes a Deep Learning based edge detector, which is inspired on both HED
(Holistically-Nested Edge Detection) and Xception networks. The proposed approach …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a Bi …

Visual recognition requires rich representations that span levels from low to high, scales
from small to large, and resolutions from fine to coarse. Even with the depth of features in a …