Results 61 to 70 of about 18,989 (190)

Automated diabetic retinopathy grading and lesion detection based on the modified R‐FCN object‐detection algorithm

IET Computer Vision, 2020
In this work, we develop a computer‐aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images.
Jialiang Wang, Jianxu Luo, Bin Liu, Rui Feng, Lina Lu, Haidong Zou   +5 more
doaj   +1 more source

From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification

Remote Sensing, 2021
Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and
Qian Liu, Zebin Wu, Xiuping Jia, Yang Xu, Zhihui Wei   +4 more
doaj   +1 more source

A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition

, 2017
This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g.
Stolkin, Rustam, Sun, Li, Zhao, Cheng
core   +1 more source

Why Autonomous Vehicles Are Not Ready Yet: A Multi‐Disciplinary Review of Problems, Attempted Solutions, and Future Directions

Journal of Field Robotics, EarlyView.
ABSTRACT Personal autonomous vehicles can sense their surrounding environment, plan their route, and drive with little or no involvement of human drivers. Despite the latest technological advancements and the hopeful announcements made by leading entrepreneurs, to date no personal vehicle is approved for road circulation in a “fully” or “semi ...
Xingshuai Dong, Massimiliano Cappuccio, Fady Alnajjar, Luqman Ali, Hamad Al Jassmi, Essam Debie, Milad Ghasrikhouzani, Alessandro Lanteri, Tate McGregor, Oleksandra Molloy, Alice Plebe, Michael Regan, Eduardo Sandoval, Dongmo Zhang   +13 more
wiley   +1 more source

Distributed AutoML framework for multi‐objective optimization of concrete crack segmentation models

Structural Concrete, EarlyView.
Abstract Monitoring cracks in concrete surfaces is essential for structural safety. While machine vision techniques have received significant interest in this domain, selecting optimal models and tuning hyperparameters remain challenging. This paper proposes a Distributed Automated Machine Learning (AutoML) framework for efficiently designing and ...
Armin Dadras Eslamlou, Shiping Huang, Belén Riveiro, M. Z. Naser   +3 more
wiley   +1 more source

Tire Defect Detection Using Fully Convolutional Network

IEEE Access, 2019
A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN ...
Ren Wang, Qiang Guo, Shanmei Lu, Caiming Zhang   +3 more
doaj   +1 more source

Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Ice concentration estimates are typically acquired from algorithms using passive microwave satellite data, and from image analysis charts, but these have limitations. Estimates acquired from passive microwave data have coarse spatial resolution, may have
Keerthijan Radhakrishnan, K. Andrea Scott, David A. Clausi   +2 more
doaj   +1 more source

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam, Basit Raza, Muhammad Faheem, Aadil Raza   +3 more
wiley   +1 more source

Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines

Algorithms
Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results ...
Zhaofeng Liu, Xiaoqing Zheng, Anke Xue, Ming Ge, Aipeng Jiang   +4 more
doaj   +1 more source

ECG‐TransCovNet: A hybrid transformer model for accurate arrhythmia detection using Electrocardiogram signals

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading to severe cardiac conditions and sudden cardiac deaths. Therefore, early and accurate detection of arrhythmias is crucial for timely intervention and potentially life‐saving treatment.
Hasnain Ali Shah, Faisal Saeed, Muhammad Diyan, Nouf Abdullah Almujally, Jae‐Mo Kang   +4 more
wiley   +1 more source