A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening

Huang, Wei; Feng, Jie; Wang, Hua; Sun, Le

doi:10.3390/ijgi9040242

Cited by 12 publications

(6 citation statements)

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“…Mask R-CNN is a convolutional neural network designed for instance segmentation tasks, and it can segment fruits from complex natural environments (Ge et al, 2019;Yu et al, 2019;Huang et al, 2020). Mask R-CNN uses ResNet50/ResNet101 as the backbone network and FPN as the neck.…”

Section: Mask R-cnn Combined With Self-calibrated Convolutionsmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Mask R-cnn Combined With Self-calibrated Convolutionsmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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“…Dense Convolutional Network (DenseNet) is another CNN family used for insect detection and classification. This neural network architecture is characterized by dense layers Huang et al, 2020. Each layer is connected to every other layer in the network (Huang et al, 2017). This creates a dense network of connections, which allows for a more efficient flow of information and a greater capacity for learning.…”

Section: Neural Network Used In Insect Detection Segmentation and Cla...mentioning

confidence: 99%

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Popescu,

Dinca,

Ichim

et al. 2023

Front. Plant Sci.

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Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production.

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“…Therefore, DenseNet represents a trend for recently published papers because of its efficiency and better ACC. The reason is that, in the initial paper [108], the authors introduced densely connected layers, thus modifying the standard CNN architecture as in Figure 20. In DenseNet, each layer is fed with additional inputs from all preceding layers and provides its own input/feature map to all subsequent layers.…”

Section: Densenetmentioning

confidence: 99%

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Popescu

Elkhatib

El-Khatib

et al. 2022

Sensors

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Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018–2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.

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A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening

Cited by 12 publications

References 50 publications

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

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