MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

Danilo Avola; Daniele Pannone

doi:10.3390/drones5040111

MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

Drones ◽

10.3390/drones5040111 ◽

2021 ◽

Vol 5 (4) ◽

pp. 111

Author(s):

Danilo Avola ◽

Daniele Pannone

Keyword(s):

Deep Learning ◽

Precision Agriculture ◽

Low Cost ◽

Semantic Segmentation ◽

Search And Rescue ◽

Small Scale ◽

Border Control ◽

Control Precision ◽

Challenging Tasks ◽

Deep Learning Model

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method.

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rajat Garg ◽

Anil Kumar ◽

Nikunj Bansal ◽

Manish Prateek ◽

Shashi Kumar

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Urban Area ◽

Urban Areas ◽

Learning Algorithms ◽

Semantic Segmentation ◽

Learning Model ◽

Machine Learning Algorithms ◽

Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation

Alexandria Engineering Journal ◽

10.1016/j.aej.2020.10.046 ◽

2021 ◽

Vol 60 (1) ◽

pp. 1231-1239

Author(s):

Nasser Alalwan ◽

Amr Abozeid ◽

AbdAllah A. ElHabshy ◽

Ahmed Alzahrani

Keyword(s):

Deep Learning ◽

Medical Image ◽

Semantic Segmentation ◽

Learning Model ◽

Deep Learning Model

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Visual Saliency Prediction Based on Deep Learning

Information ◽

10.3390/info10080257 ◽

2019 ◽

Vol 10 (8) ◽

pp. 257 ◽

Cited By ~ 7

Author(s):

Bashir Ghariba ◽

Mohamed S. Shehata ◽

Peter McGuire

Keyword(s):

Deep Learning ◽

Saliency Detection ◽

Visual Saliency ◽

Semantic Segmentation ◽

Input Image ◽

Human Eye ◽

Proposed Model ◽

Global Accuracy ◽

Visual Saliency Detection ◽

Deep Learning Model

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

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Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving

Sensors ◽

10.3390/s21238072 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8072

Author(s):

Yu-Bang Chang ◽

Chieh Tsai ◽

Chang-Hong Lin ◽

Poki Chen

Keyword(s):

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Attention Mechanism ◽

Trade Off ◽

Segmentation Methods ◽

General Semantic ◽

Deep Learning Model

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.

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pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.617366 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shihua Li ◽

Kai Yu ◽

Guandi Wu ◽

Qingfeng Zhang ◽

Panqin Wang ◽

...

Keyword(s):

Deep Learning ◽

Low Cost ◽

Redox Homeostasis ◽

Cysteine Modification ◽

Biological Regulation ◽

Post Translational Modifications ◽

Learning Framework ◽

Multiple Protein ◽

Regulation Mechanisms ◽

Deep Learning Model

Thiol groups on cysteines can undergo multiple post-translational modifications (PTMs), acting as a molecular switch to maintain redox homeostasis and regulating a series of cell signaling transductions. Identification of sophistical protein cysteine modifications is crucial for dissecting its underlying regulatory mechanism. Instead of a time-consuming and labor-intensive experimental method, various computational methods have attracted intense research interest due to their convenience and low cost. Here, we developed the first comprehensive deep learning based tool pCysMod for multiple protein cysteine modification prediction, including S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation. Experimentally verified cysteine sites curated from literature and sites collected by other databases and predicting tools were integrated as benchmark dataset. Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. Cross-validations indicated our model showed excellent robustness and outperformed existing tools, which was able to achieve an average AUC of 0.793, 0.807, 0.796, 0.793, and 0.876 for S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation, demonstrating pCysMod was stable and suitable for protein cysteine modification prediction. Besides, we constructed a comprehensive protein cysteine modification prediction web server based on this model to benefit the researches finding the potential modification sites of their interested proteins, which could be accessed at http://pcysmod.omicsbio.info. This work will undoubtedly greatly promote the study of protein cysteine modification and contribute to clarifying the biological regulation mechanisms of cysteine modification within and among the cells.

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Deep learning enabled smart mats as a scalable floor monitoring system

Nature Communications ◽

10.1038/s41467-020-18471-z ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 12

Author(s):

Qiongfeng Shi ◽

Zixuan Zhang ◽

Tianyiyi He ◽

Zhongda Sun ◽

Bingjie Wang ◽

...

Keyword(s):

Deep Learning ◽

Monitoring System ◽

Data Analytics ◽

Low Cost ◽

Computational Cost ◽

Sensory Information ◽

Small Scale ◽

Large Power ◽

Smart Building ◽

Sensory Data

Abstract Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

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Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images

Physica Medica ◽

10.1016/j.ejmp.2020.09.004 ◽

2020 ◽

Vol 78 ◽

pp. 93-100

Author(s):

Takafumi Nemoto ◽

Natsumi Futakami ◽

Masamichi Yagi ◽

Etsuo Kunieda ◽

Takeshi Akiba ◽

...

Keyword(s):

Prostate Cancer ◽

Radiation Therapy ◽

Deep Learning ◽

Low Cost ◽

Semantic Segmentation ◽

Ct Images ◽

Therapy Planning ◽

Radiation Therapy Planning ◽

Planning Ct

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Precision agriculture technology adoption: a qualitative study of small-scale commercial “family farms” located in the North China Plain

Precision Agriculture ◽

10.1007/s11119-021-09839-2 ◽

2021 ◽

Author(s):

Helen Kendall ◽

Beth Clark ◽

Wenjing Li ◽

Shan Jin ◽

Glyn. D. Jones ◽

...

Keyword(s):

Qualitative Study ◽

Precision Agriculture ◽

Low Cost ◽

Information Provision ◽

Small Scale ◽

Family Farms ◽

Family Farmers ◽

The North ◽

Practical Support ◽

Support Mechanisms

AbstractPrecision agriculture (PA) technologies offer a potential solution to food security and environmental challenges but, will only be successful if adopted by farmers. Adoption in China lags behind that in some developed agricultural economies despite scientifically proven benefits of PA technologies for Chinese agriculture. Adoption is dependent on farmer attitudes and perceptions towards PA technologies. An exploratory qualitative study using in-depth interviews was conducted with Chinese arable farmers (n = 27) to explore their perceptions towards and adoption intentions of PA technologies in two Chinese provinces (Hebei and Shandong). A thematic analysis revealed five central themes to have emerged from the data, these were: “socio-political landscape”, “farming culture”, “agricultural challenges”, “adoption intentions (barriers/facilitators” and “practical support mechanisms”. All were likely to influence the level and rate of adoption of PA technologies amongst family farmers in China. The research revealed an openness to the potential of PA technologies amongst family farmers, although there was heterogeneity in the perceptions of PA technology and willingness to adopt. Improved rates of adoption will be achieved by reducing the barriers to adoption, including the need for low-cost PA applications that can be applied at small scale, improved information provision, financial support mechanisms including more accessible subsidies and credit, and reliable, regulated and affordable service provision.

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