Related references
Note: Only part of the references are listed.
Article
Engineering, Civil
Xi Weng et al.
Summary: This paper proposes a novel Deep Multi-branch Aggregation Network (DMA-Net) for real-time semantic segmentation in street scenes. The DMA-Net achieves a good tradeoff between segmentation quality and speed by effectively aggregating different levels of feature maps and capturing multi-scale information. Experimental results on Cityscapes and CamVid datasets demonstrate the superior performance of DMA-Net.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xuegang Hu et al.
Summary: The proposed joint pyramid attention network (JPANet) is designed for real-time semantic segmentation, incorporating a joint feature pyramid (JFP) module, spatial detail extraction (SDE) module, and bilateral feature fusion (BFF) module to achieve high segmentation accuracy and frame rate on high-resolution images.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Civil
Guangwei Gao et al.
Summary: In this study, a novel lightweight network MSCFNet is proposed for real-time semantic segmentation applications, which addresses the trade-off between accuracy, inference speed, and model size by utilizing a multi-scale context fusion scheme and an asymmetric encoder-decoder architecture. By simplifying computation and applying efficient attention modules, the network achieves high segmentation accuracy and faster segmentation efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Cunjun Xiao et al.
Summary: The proposed LSPANet addresses the challenges faced by semantic segmentation networks in utilizing information and handling small-scale objects. By incorporating dual-branch decoding fusion and spatial pixel cross-correlation blocks, it achieves precise adjustment of each pixel value and faster inference speed.
IMAGE AND VISION COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
Saeid Asgari Taghanaki et al.
Summary: This review categorizes leading deep learning-based medical and non-medical image segmentation solutions into six main groups and provides a comprehensive review of each group's contributions. It analyzes the limitations of current approaches and presents potential future research directions for improving semantic image segmentation.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Xiaochen Hao et al.
Summary: This paper presents a new deep convolutional network WFDCNet, which can improve inference speed while maintaining high accuracy, by using FCS module and LAPF module. Superior performance is demonstrated on multiple datasets, especially achieving 73.7% mIoU on Cityscapes dataset.
IMAGE AND VISION COMPUTING
(2021)
Article
Engineering, Civil
Di Feng et al.
Summary: Deep learning is driving recent advancements in perception for autonomous driving through the fusion of multiple sensors, but questions regarding network architecture design, fusion timing, and methods remain open. This review aims to systematically summarize methodologies for deep multi-modal object detection and semantic segmentation in autonomous driving, while also discussing challenges and open questions. The reviewed study provides an overview of the topic, fusion methodologies, and offers an interactive online platform for further exploration.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Hsiang-Yu Han et al.
Summary: Advanced Driver Assistance Systems (ADAS) comprises object detection and image segmentation, with deep learning-based image semantic segmentation showing better performance for guiding vehicles in complex environments. The proposed Edgenet achieves over 70% mean IOU on Cityscapes dataset and runs at over 30 FPS on a single GTX Titan X (Maxwell) GPU.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xiangyan Tang et al.
Summary: Semantic segmentation research in IoT has been challenging, with recent trends focused on fusing multi-level features and multi-scale context information. However, existing literature faces issues of resource-consuming feature extraction and semantic gap between multi-layer features. To address these challenges, a novel IoT-perceptive Dual Feature Fusion Network (DFFNet) is proposed for effective utilization of features and context information.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Mingxi Zhuang et al.
Summary: This paper proposes a lightweight network LRDNet for a better balance between computational speed and segmentation accuracy. The network includes a refined dual attention decoder, asymmetric module, and deep convolution to improve efficiency in feature extraction, achieving impressive results on the Cityscapes dataset.
Article
Computer Science, Artificial Intelligence
Tianyi Wu et al.
Summary: The research presents a lightweight and efficient semantic segmentation network CGNet, which captures contextual information in all stages of the network through context-guided technology, designed specifically to increase segmentation accuracy and reduce memory usage. Experimental results show that CGNet achieves good performance on the Cityscapes dataset.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Jiawei Wang et al.
APPLIED INTELLIGENCE
(2020)
Article
Engineering, Civil
Eduardo Romera et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Liang-Chieh Chen et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Article
Computer Science, Artificial Intelligence
Vijay Badrinarayanan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2017)
