Review the state-of-the-art technologies of semantic segmentation based on deep learning
Y Mo, Y Wu, X Yang, F Liu, Y Liao - Neurocomputing, 2022 - Elsevier
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
Deep learning sensor fusion for autonomous vehicle perception and localization: A review
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
Deep learning for image and point cloud fusion in autonomous driving: A review
Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
Autonomous driving: cognitive construction and situation understanding
Autonomous vehicle is a kind of typical complex artificial intelligence system. In current
research of autonomous driving, the most widely adopted technique is to use a basic …
research of autonomous driving, the most widely adopted technique is to use a basic …
Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis
Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two
of the most widely used modalities in the clinical diagnosis and management of retinal …
of the most widely used modalities in the clinical diagnosis and management of retinal …
Two-view fusion based convolutional neural network for urban road detection
In this paper, we propose a two-view fusion based convolutional neural network to estimate
road areas in urban environments with LiDAR point clouds as input only. The proposed …
road areas in urban environments with LiDAR point clouds as input only. The proposed …
Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks
Autonomous vehicles rely on perceiving the road environment through a perception pipeline
fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera …
fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera …
Lidar guided small obstacle segmentation
Detecting small obstacles on the road is critical for autonomous driving. In this paper, we
present a method to reliably detect such obstacles through a multi-modal framework of …
present a method to reliably detect such obstacles through a multi-modal framework of …
A survey of 3D point cloud and deep learning-based approaches for scene understanding in autonomous driving
L Wang, Y Huang - IEEE Intelligent Transportation Systems …, 2021 - ieeexplore.ieee.org
Supported by the advancement of deep learning (DL) techniques and a massive procession
of sensor technology, feature learning from 3D lidar data has led to rapid development in the …
of sensor technology, feature learning from 3D lidar data has led to rapid development in the …