Helmet use detection of tracked motorcycles using CNN-based multi-task learning.

Lin, Hanhe; Deng, Jeremiah D.; Albers, Deike; Siebert, Felix Wilhelm

doi:10.1109/access.2020.3021357

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. (2020)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2020. A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. Sensors [online], 20(24): deep learning, artificial neural networks and sensors for fault diagnosis, article 7205. Available from: https://doi.org/10.3390/s20247205

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequ... Read More about A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions..

EACOFT: an energy-aware correlation filter for visual tracking. (2020)
Journal Article
LIU, Q., REN, J., WANG, Y., WU, Y., SUN, H. and ZHAO, H. 2021. EACOFT: an energy-aware correlation filter for visual tracking. Pattern recognition [online], 112, article ID 107766. Available from: https://doi.org/10.1016/j.patcog.2020.107766

Correlation filter based trackers attribute to its calculation in the frequency domain can efficiently locate targets in a relatively fast speed. This characteristic however also limits its generalization in some specific scenarios. The reasons that... Read More about EACOFT: an energy-aware correlation filter for visual tracking..

DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications. (2020)
Journal Article
XI, Y., JIA, W., ZHENG, J., FAN, X., XIE, Y., REN, J. and HE, X. 2021. DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications. IEEE Journal of selected topics in applied earth observations and remote sensing [online], 14, pages 1705-1716. Available from: https://doi.org/10.1109/JSTARS.2020.3043109

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited att... Read More about DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications..

In-house deep environmental sentience for smart homecare solutions toward ageing society. (2020)
Conference Proceeding
EASOM, P., BOURIDANE, A., QIANG, F., DOWNS, C. and JIANG, R. 2020. In-house deep environmental sentience for smart homecare solutions toward ageing society. In Proceedings of 2020 International conference machine learning and cybernetics (ICMLC 2020), 4 December 2020, [virtual conference]. Piscataway: IEEE [online], pages 261-266. Available from: https://doi.org/10.1109/ICMLC51923.2020.9469531

With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suff... Read More about In-house deep environmental sentience for smart homecare solutions toward ageing society..

Object recognition using enhanced particle swarm optimization. (2020)
Conference Proceeding
WILLIS, M., ZHANG, L., LIU, H., XIE, H. and MISTRY, L. 2020. Object recognition using enhanced particle swarm optimization. In Proceedings of 2020 International conference machine learning and cybernetics (ICMLC 2020), 4 December 2020, [virtual conference]. Piscataway: IEEE [online], pages 241-246. Available from: https://doi.org/10.1109/ICMLC51923.2020.9469584

The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation a... Read More about Object recognition using enhanced particle swarm optimization..

Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm). (2020)
Journal Article
ROHAN, A., RAOUF, I. and KIM, H.S. 2020. Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm). Sensors [online], 20(23), article 6845. Available from: https://doi.org/10.3390/s20236845

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are... Read More about Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm)..

Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. (2020)
Journal Article
CHAI, Y., REN, J., HWANG, B., WANG, J., FAN, D., YAN, Y. and ZHU, S. 2021. Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 577-586. Available from: https://doi.org/10.1109/jstars.2020.3040614

Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods su... Read More about Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images..

Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. (2020)
Journal Article
MA, P., REN, J., ZHAO, H., SUN, G., MURRAY, P. and ZHENG, J. 2021. Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 1233-1245. Available from: https://doi.org/10.1109/JSTARS.2020.3040699

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss fr... Read More about Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images..

Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. (2020)
Journal Article
FU, H., SUN, G., REN, J., ZHANG, A. and JIA, X. 2020. Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE transactions on geoscience and remote sensing [online], 60, article 5500214. Available from: https://doi.org/10.1109/TGRS.2020.3034656

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–... Read More about Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery..

Iterative enhanced multivariance products representation for effective compression of hyperspectral images. (2020)
Journal Article
TUNA, S., TÖREYIN, B.U., REN, J., ZHAO, H. and MARSHALL, S. 2021. Iterative enhanced multivariance products representation for effective compression of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 59(11), pages 9569-9584. Available from: https://doi.org/10.1109/TGRS.2020.3031016

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method b... Read More about Iterative enhanced multivariance products representation for effective compression of hyperspectral images..

Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. (2020)
Journal Article
HASAN, M.J., SHON, D., IM, K., CHOI, H.-K., YOO, D.-S. and KIM, J.-M. 2020. Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. Applied sciences [online], 10(21): medical signal and image processing, article 7639. Available from: https://doi.org/10.3390/app10217639

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., t... Read More about Sleep state classification using power spectral density and residual neural network with multichannel EEG signals..

Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images. (2020)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., BANU, S.F., CHAMBON, S., RADEVA, P. and PUIG, D. 2020. Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics letters [online], 56(24), pages 1298-1301. Available from: https://doi.org/10.1049/el.2020.1962

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on invert... Read More about Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images..

Generic wavelet‐based image decomposition and reconstruction framework for multi‐modal data analysis in smart camera applications. (2020)
Journal Article
YAN, Y., LIU, Y., YANG, M., ZHAO, H., CHAI, Y. and REN, J. 2020. Generic wavelet-based image decomposition and reconstruction framework for multi-modal data analysis in smart camera applications. IET computer vision [online], 14(7): computer vision for smart cameras and camera networks, pages 471-479. Available from: https://doi.org/10.1049/iet-cvi.2019.0780

Effective acquisition, analysis and reconstruction of multi-modal data such as colour and multi-/hyper-spectral imagery is crucial in smart camera applications, where wavelet-based coding and compression of images are highly demanded. Many existing d... Read More about Generic wavelet‐based image decomposition and reconstruction framework for multi‐modal data analysis in smart camera applications..

Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. Sensors [online], 20(20), article 5786. Available from: https://doi.org/10.3390/s20205786

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim... Read More about Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss..

Human pose estimation-based real-time gait analysis using convolutional neural network. (2020)
Journal Article
ROHAN, A., RABAH, M., HOSNY, T. and KIM, S.-H. 2020. Human pose estimation-based real-time gait analysis using convolutional neural network. IEEE access [online] 8, pages 191542-191550. Available from: https://doi.org/10.1109/ACCESS.2020.3030086

Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized dev... Read More about Human pose estimation-based real-time gait analysis using convolutional neural network..

SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images. (2020)
Journal Article
FANG, Z., REN, J., SUN, H., MARSHALL, S., HAN, J. and ZHAO, H. 2020. SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images. Remote sensing [online], 12(19), article 3225. Available from: https://doi.org/10.3390/rs12193225

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned ho... Read More about SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images..

A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. (2020)
Journal Article
REN, J., YAN, Y., ZHAO, H., MA, P., ZABALZA, J., HUSSAIN, Z., LUO, S., DAI, Q., ZHAO, S., SHEIKH, A., HUSSAIN, A. and LI, H. 2020. A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. IEEE Journal of biomedical and health informatics [online], 24(12), pages 3551-3563. Available from: https://doi.org/10.1109/jbhi.2020.3027987

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective mana... Read More about A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19..

Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. (2020)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2021. Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. Measurement [online], 168, article 108478. Available from: https://doi.org/10.1016/j.measurement.2020.108478

In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to conv... Read More about Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions..

Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. (2020)
Journal Article
CHEN, R., YU, Y., XIE, S., ZHAO, H., LIU, S., REN, J. and TAN, H.-Z. 2020. Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. Sustainability [online], 12(17), article 7192. Available from: https://doi.org/10.3390/su12177192

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the exist... Read More about Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism..

Helmet use detection of tracked motorcycles using CNN-based multi-task learning. (2020)
Journal Article
LIN, H., DENG, J.D., ALBERS, D. and SIEBERT, F.W. 2020. Helmet use detection of tracked motorcycles using CNN-based multi-task learning. IEEE access [online], 8, pages 162073-162084. Available from: https://doi.org/10.1109/access.2020.3021357

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilit... Read More about Helmet use detection of tracked motorcycles using CNN-based multi-task learning..

All Outputs (36)