scholarly journals Implementation of feature extraction and deep learning-based ensemble classifier for interference mitigation in radar signals

2021 ◽  
Vol 24 (2) ◽  
pp. 1195
Author(s):  
N. Durga Indira ◽  
M. Venu Gopala Rao
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Long Range ◽  
Target Detection ◽  
Interference Mitigation ◽  
Autonomous Driving ◽  
Ensemble Classifier ◽  
Accurate Performance ◽  
Mitigation Techniques ◽  
The One

In automotive vehicles, radar is the one of the component for autonomous driving, used for target detection and long-range sensing. Whereas interference exists in signals, noise increases and it effects severely while detecting target objects. For these reasons, various interference mitigation techniques are implemented in this paper. By using these mitigation techniques interference and noise are reduced and original signals are reconstructed. In this paper, we proposed a method to mitigate interference in signal using deep learning. The proposed method provides the best and accurate performance in relate to the various interference conditions and gives better accuracy compared with other existing methods.

Deep Learning-Based Lane Marking Detection using A2-LMDet

2020 ◽  
Vol 2674 (11) ◽  
pp. 625-635
Author(s):  
Chunmian Lin ◽  
Lin Li ◽  
Zhixing Cai ◽  
Kelvin C. P. Wang ◽  
Danny Xiao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Pavement Management ◽  
Feature Representation ◽  
Imaging Data ◽  
Lane Marking ◽  
Accurate Performance ◽  
And Performance ◽  
The One

Automated lane marking detection is essential for advanced driver assistance system (ADAS) and pavement management work. However, prior research has mostly detected lane marking segments from a front-view image, which easily suffers from occlusion or noise disturbance. In this paper, we aim at accurate and robust lane marking detection from a top-view perspective, and propose a deep learning-based detector with adaptive anchor scheme, referred to as A2-LMDet. On the one hand, it is an end-to-end framework that fuses feature extraction and object detection into a single deep convolutional neural network. On the other hand, the adaptive anchor scheme is designed by formulating a bilinear interpolation algorithm, and is used to guide specific-anchor box generation and informative feature extraction. To validate the proposed method, a newly built lane marking dataset contained 24,000 high-resolution laser imaging data is further developed for case study. Quantitative and qualitative results demonstrate that A2-LMDet achieves highly accurate performance with 0.9927 precision, 0.9612 recall, and a 0.9767 [Formula: see text] score, which outperforms other advanced methods by a considerable margin. Moreover, ablation analysis illustrates the effectiveness of the adaptive anchor scheme for enhancing feature representation and performance improvement. We expect our work will help the development of related research.

scholarly journals Automated color detection in orchids using color labels and deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259036
Author(s):  
Diah Harnoni Apriyanti ◽  
Luuk J. Spreeuwers ◽  
Peter J. F. Lucas ◽  
Raymond N. J. Veldhuis
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Recognition System ◽  
Ensemble Classifier ◽  
Ensemble Classifiers ◽  
Detection Model ◽  
Color Detection ◽  
Multi Class Classification ◽  
The One ◽  
Color Scheme

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

scholarly journals Practical Approaches to Target Detection in Long Range and Low Quality Infrared Videos

2021 ◽  
Vol 12 (3) ◽  
pp. 01-16
Author(s):  
Chiman Kwan ◽  
David Gribben
Keyword(s):  
Deep Learning ◽  
Long Range ◽  
Target Detection ◽  
Experimental Results ◽  
Target Size ◽  
Small Target ◽  
Performance Improvements ◽  
Texture Information ◽  
Learning Techniques ◽  
Small Targets

It is challenging to detect vehicles in long range and low quality infrared videos using deep learning techniques such as You Only Look Once (YOLO) mainly due to small target size. This is because small targets do not have detailed texture information. This paper focuses on practical approaches for target detection in infrared videos using deep learning techniques. We first investigated a newer version of You Only Look Once (YOLO v4). We then proposed a practical and effective approach by training the YOLO model using videos from longer ranges. Experimental results using real infrared videos ranging from 1000 m to 3500 m demonstrated huge performance improvements. In particular, the average detection percentage over the six ranges of 1000 m to 3500 m improved from 54% when we used the 1500 m videos for training to 95% if we used the 3000 m videos for training.

scholarly journals Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5283
Author(s):  
Tahira Nazir ◽  
Marriam Nawaz ◽  
Junaid Rashid ◽  
Rabbia Mahum ◽  
Momina Masood ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Screening Method ◽  
Region Of Interest ◽  
Disease Classification ◽  
The Other ◽  
Training Data ◽  
Eye Disease ◽  
Feature Extraction Method ◽  
The One

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Interference Mitigation Techniques Applied to LTE Femtocells Systems

2019 ◽  
Vol 28 (2) ◽  
pp. 117-128
Author(s):  
Mohamed Shalaby ◽  
Mona Shokair ◽  
Nagy Messiha
Keyword(s):  
Interference Mitigation ◽  
Mitigation Techniques

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Sign in / Sign up

Export Citation Format

Share Document