activity
20192021
most citedGraph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms

108 citations · 145 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2021108 cited

Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms

Rui Fan, Hengli Wang, Yuan Wang +2

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understan…

cs.CV20205 cited

Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation

Rui Fan, Umar Ozgunalp, Yuan Wang +2

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by…

cs.CV202028 cited

Computer Stereo Vision for Autonomous Driving

Rui Fan, Li Wang, Mohammud Junaid Bocus +1

As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures. With the use of tiny but ful…

cs.CV2020

ATG-PVD: Ticketing Parking Violations on A Drone

Hengli Wang, Yuxuan Liu, Huaiyang Huang +8

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framewo…

cs.CV2020

Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

Rui Fan, Hengli Wang, Bohuan Xue +4

This paper proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal estimator (SNE), which is designed for structured range sensor data, e.g., depth/dispar…

cs.CV20192 cited

PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation

Rui Fan, Yuan Wang, Lei Qiao +5

Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segme…