scholarly journals An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4709
Author(s):  
Bin Wang ◽  
Yinjuan Gu
Keyword(s):  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Great Success ◽  
Learning Technology ◽  
Detection Algorithms ◽  
Special Domain ◽  
Hard Samples

With the development of artificial intelligence and big data analytics, an increasing number of researchers have tried to use deep-learning technology to train neural networks and achieved great success in the field of vehicle detection. However, as a special domain of object detection, vehicle detection in aerial images still has made limited progress because of low resolution, complex backgrounds and rotating objects. In this paper, an improved feature-balanced pyramid network (FBPN) has been proposed to enhance the network’s ability to detect small objects. By combining FBPN with modified faster region convolutional neural network (faster-RCNN), a vehicle detection framework for aerial images is proposed. The focal loss function is adopted in the proposed framework to reduce the imbalance between easy and hard samples. The experimental results based on the VEDIA, USCAS-AOD, and DOTA datasets show that the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

scholarly journals Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

2020 ◽  
Author(s):  
Ahmed Tageldin ◽  
Dalia Adly ◽  
Hassan Mostafa ◽  
Haitham S Mohammed
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Prediction Accuracy ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Total Period ◽  
Cotton Leafworm ◽  
Main Challenge ◽  
The Impact

AbstractThe use of technology in agriculture has grown in recent years with the era of data analytics affecting every industry. The main challenge in using technology in agriculture is identification of effectiveness of big data analytics algorithms and their application methods. Pest management is one of the most important problems facing farmers. The cotton leafworm, Spodoptera littoralis (Boisd.) (CLW) is one of the major polyphagous key pests attacking plants includes 73 species recorded at Egypt. In the present study, several machine learning algorithms have been implemented to predict plant infestation with CLW. The moth of CLW data was weekly collected for two years in a commercial hydroponic greenhouse. Furthermore, among other features temperature and relative humidity were recorded over the total period of the study. It was proven that the XGBoost algorithm is the most effective algorithm applied in this study. Prediction accuracy of 84 % has been achieved using this algorithm. The impact of environmental features on the prediction accuracy was compared with each other to ensure a complete dataset for future results. In conclusion, the present study provided a framework for applying machine learning in the prediction of plant infestation with the CLW in the greenhouses. Based on this framework, further studies with continuous measurements are warranted to achieve greater accuracy.

Privacy-Preserving in Big Data Analytics: State of the Art

2020 ◽  
Author(s):  
Hidayath Ali Baig ◽  
Dr. Yogesh Kumar Sharma ◽  
Syed Zakir Ali
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Privacy Preserving

scholarly journals Industry 4.0 Applications for Medical/Healthcare Services

2021 ◽  
Vol 10 (3) ◽  
pp. 43
Author(s):  
Shuva Paul ◽  
Muhtasim Riffat ◽  
Abrar Yasir ◽  
Mir Nusrat Mahim ◽  
Bushra Yasmin Sharnali ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Data Analytics ◽  
Industrial Revolution ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Start Ups ◽  
Medical Healthcare

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

scholarly journals Multi Comm_Plus: A Community Detection System for Identification of Community in Multi-Dimensional Networks

2016 ◽  
Vol 4 (05) ◽  
pp. 9879-9884
Author(s):  
Dhanya Sudhakaran ◽  
Shini Renjith
Keyword(s):  
Big Data ◽  
Real Time ◽  
Community Detection ◽  
Data Analytics ◽  
Selection Criteria ◽  
Large Scale ◽  
Detection System ◽  
Big Data Analytics ◽  
Detection Algorithms ◽  
Large Scale Networks

Community detection is a common problem in graph and big data analytics. It consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. Community detection algorithms in literature proves to be less efficient, as it leads to generation of communities with noisy interactions. To address this limitation, there is a need to develop a system which identifies the best community among multi-dimensional networks based on relevant selection criteria and dimensionality of entities, thereby eliminating the noisy interactions in a real-time environment.

scholarly journals Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

2017 ◽  
Vol 13 (4) ◽  
pp. 1891-1899 ◽  
Author(s):  
Zhihan Lv ◽  
Houbing Song ◽  
Pablo Basanta-Val ◽  
Anthony Steed ◽  
Minho Jo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Future Research ◽  
Next Generation ◽  
Research Topics

scholarly journals LR-CNN: LOCAL-AWARE REGION CNN FOR VEHICLE DETECTION IN AERIAL IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 381-388 ◽  
Author(s):  
W. Liao ◽  
X. Chen ◽  
J. Yang ◽  
S. Roth ◽  
M. Goesele ◽  
...  
Keyword(s):  
State Of The Art ◽  
Vehicle Detection ◽  
Local Feature ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Learning Ability ◽  
Location Information ◽  
Translation Invariance ◽  
Arbitrary Orientation ◽  
Art Object

Abstract. State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs’ features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset.

scholarly journals MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

2020 ◽  
Vol 12 (19) ◽  
pp. 3118
Author(s):  
Danqing Xu ◽  
Yiquan Wu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Target Detection ◽  
State Of The Art ◽  
Receptive Fields ◽  
Aerial Images ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Small Targets

High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

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