scholarly journals Deep Features based Multilingual Text Detection and Recognition in Video Frames

2019 ◽  
Vol 8 (4) ◽  
pp. 9420-9429
Keyword(s):  
High Frequency ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Sliding Window ◽  
Text Detection ◽  
Discrete Wavelet ◽  
Video Frames ◽  
South Indian ◽  
Improved Performance ◽  
Multilingual Text

One of the major causes for achieving poor text detection results in video frames are complex background, illumination and de-blurring of the frames and it is also important challenges for the researchers to overcome such problems. Therefore, motivated from this kind of observation from recent survey, we propose a text detection method based on Deep Neural Networks known as TextBoxes which is capable of detecting text in video frames with improved performance when compared to state-of-the-art techniques. In parallel this we also propose a Text candidate detection for video frames and scene images by extracting words based on Automatic Window Detection by making use of Discrete Wavelet Transform (DWT) with the sliding window for extracting high frequency sub bands for each sliding window. K-means clustering technique has been used to obtain the text components and to decrease the background complexity and noise. Six-layer convolutional neural network model has been designed to recognize the text in multilingual images. Experiments for text detection are done on our own multilingual South Indian database, ICDAR-2015 Videos, YVT videos, SVT, and MSRA Scene datasets and demonstrated in terms of Recall, Precision and F-measure and for recognition ICDAR-2015 Videos, ICDAR 2011 and SVT scene images

scholarly journals An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

2020 ◽  
Vol 10 (11) ◽  
pp. 3922 ◽  
Author(s):  
Guishuo Wang ◽  
Xiaoli Wang ◽  
Chen Zhao
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
High Frequency Part

The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

scholarly journals Domain-Specific On-Device Object Detection Method

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 77
Author(s):  
Seongju Kang ◽  
Jaegi Hwang ◽  
Kwangsue Chung
Keyword(s):  
Object Detection ◽  
High Frequency ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Significant Activity ◽  
Detection Model ◽  
Domain Specific ◽  
Conventional Models ◽  
F Measure ◽  
Constrained Devices

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Sign in / Sign up

Export Citation Format

Share Document