scholarly journals Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Zhimin Lin ◽  
Ying Zeng ◽  
Hui Gao ◽  
Li Tong ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Target Detection ◽  
Visual Presentation ◽  
Research Direction ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Single Trial ◽  
Target Image ◽  
Detection Algorithms ◽  
New Research

Target image detection based on a rapid serial visual presentation (RSVP) paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.

A Robust Blind Image Forensic-Detection Method based on Fuzzy Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 5926-5929
Keyword(s):  
Research Direction ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
Forensic Investigation ◽  
Image Forensic ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Tampered Image ◽  
New Research ◽  
Blind Image

Blind forensic-investigation in a digital image is a new research direction in image security. It aims to discover the altered image content without any embedded security scheme. Block and key point based methods are the two dispensation options in blind image forensic investigation. Both the techniques exhibit the best performance to reveal the tampered image. The success of these methods is limited due to computational complexity and detection accuracy against various image distortions and geometric transformation operations. This article introduces different blind image tampering methods and introduces a robust image forensic investigation method to determine the copy-move tampered image by means of fuzzy logic approach. Empirical outcomes facilitate that the projected scheme effectively classifies copy-move type of forensic images as well as blurred tampered image. Overall detection accuracy of this method is high over the existing methods.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Angle Distance-Based Hierarchical Background Separation Method for Hyperspectral Imagery Target Detection

2020 ◽  
Vol 12 (4) ◽  
pp. 697 ◽  
Author(s):  
Xiaohui Hao ◽  
Yiquan Wu ◽  
Peng Wang
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Original Data ◽  
Separation Method ◽  
Detection Accuracy ◽  
Target Information ◽  
Detection Algorithms ◽  
Target Spectrum ◽  
Candidate Target ◽  
The Given

Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.

Armored Target Detection in Battlefield Environment Based on Top-Down Aggregation Network and Hierarchical Scale Optimization

2019 ◽  
Vol 33 (04) ◽  
pp. 1950007 ◽  
Author(s):  
Haoze Sun ◽  
Tianqing Chang ◽  
Lei Zhang ◽  
Guozhen Yang ◽  
Bin Han ◽  
...  
Keyword(s):  
Target Detection ◽  
Region Of Interest ◽  
Recall Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Top Down ◽  
Long Distance ◽  
General Object ◽  
Battlefield Environment

Armored equipment plays a crucial role in the ground battlefield. The fast and accurate detection of enemy armored targets is significant to take the initiative in the battlefield. Comparing to general object detection and vehicle detection, armored target detection in battlefield environment is more challenging due to the long distance of observation and the complicated environment. In this paper, an accurate and robust automatic detection method is proposed to detect armored targets in battlefield environment. Firstly, inspired by Feature Pyramid Network (FPN), we propose a top-down aggregation (TDA) network which enhances shallow feature maps by aggregating semantic information from deeper layers. Then, using the proposed TDA network in a basic Faster R-CNN framework, we explore the further optimization of the approach for armored target detection: for the Region of Interest (RoI) Proposal Network (RPN), we propose a multi-branch RPNs framework to generate proposals that match the scale of armored targets and the specific receptive field of each aggregated layer and design hierarchical loss for the multi-branch RPNs; for RoI Classifier Network (RCN), we apply RoI pooling on the single finest scale feature map and construct a light and fast detection network. To evaluate our method, comparable experiments with state-of-art detection methods were conducted on a challenging dataset of images with armored targets. The experimental results demonstrate the effectiveness of the proposed method in terms of detection accuracy and recall rate.

scholarly journals Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints

2018 ◽  
Vol 10 (9) ◽  
pp. 1415 ◽  
Author(s):  
Yanni Dong ◽  
Bo Du ◽  
Liangpei Zhang ◽  
Xiangyun Hu
Keyword(s):  
Target Detection ◽  
State Of The Art ◽  
Metric Learning ◽  
High Spectral Resolution ◽  
Detection Methods ◽  
Significant Information ◽  
Local Constraints ◽  
Information Theoretic ◽  
Detection Algorithms ◽  
Before And After

By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target samples and preserving the discriminative information is also a challenging problem in hyperspectral target detection. To overcome these shortcomings, this paper proposes a novel adaptive information-theoretic metric learning with local constraints (ITML-ALC) for hyperspectral target detection. The proposed method firstly uses the information-theoretic metric learning (ITML) method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs without certain assumptions, needing fewer adjusted parameters. Then, adaptively local constraints are applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, target detection decision can be made by considering both the threshold and the changes between the distances before and after metric learning. Experimental results demonstrate that the proposed method can obviously separate target samples from background ones and outperform both the state-of-the-art target detection algorithms and the other classical metric learning methods.

Study on Anomaly Detection Methods in Hyperspectral Image

2014 ◽  
Vol 631-632 ◽  
pp. 631-635
Author(s):  
Yi Ting Wang ◽  
Shi Qi Huang ◽  
Hong Xia Wang ◽  
Dai Zhi Liu
Keyword(s):  
Anomaly Detection ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Detection Methods ◽  
Imaging Data ◽  
Detection Algorithms ◽  
Sensing Technology ◽  
Anomaly Detector ◽  
And Performance ◽  
Spectral Diagnosis

Hyperspectral remote sensing technology can be used to make a correct spectral diagnosis on substances. So it is widely used in the field of target detection and recognition. However, it is very difficult to gather accurate prior information for target detect since the spectral uncertainty of objects is pervasive in existence. An anomaly detector can enable one to detect targets whose signatures are spectrally distinct from their surroundings with no prior knowledge. It becomes a focus in the field of target detection. Therefore, we study four anomaly detection algorithms and conclude with empirical results that use hyperspectral imaging data to illustrate the operation and performance of various detectors.

scholarly journals DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2743
Author(s):  
Kun Sun ◽  
Yi Liang ◽  
Xiaorui Ma ◽  
Yuanyuan Huai ◽  
Mengdao Xing
Keyword(s):  
Target Detection ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Cost ◽  
Detection Performance ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Computation Complexity ◽  
Learning Techniques

Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).

scholarly journals A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

2016 ◽  
Vol XLI-B7 ◽  
pp. 117-120
Author(s):  
S. Sharifi hashjin ◽  
A. Darvishi ◽  
S. Khazai ◽  
F. Hatami ◽  
M. Jafari houtki
Keyword(s):  
Target Detection ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Band Selection ◽  
Blind Test ◽  
Simple Method ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Selection For ◽  
Field Reflectance

In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

scholarly journals Improved YOLOv4 Marine Target Detection Combined with CBAM

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 623
Author(s):  
Huixuan Fu ◽  
Guoqing Song ◽  
Yuchao Wang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Learning ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Time Requirements ◽  
Small Targets ◽  
Sea Area

Marine target detection technology plays an important role in sea surface monitoring, sea area management, ship collision avoidance, and other fields. Traditional marine target detection algorithms cannot meet the requirements of accuracy and speed. This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. Marine target detection datasets were collected and produced and marine targets were divided into ten categories, including speedboat, warship, passenger ship, cargo ship, sailboat, tugboat, and kayak. Aiming at the problem of insufficient detection accuracy of YOLOv4’s self-built marine target dataset, a convolutional attention module is added to the YOLOv4 network to increase the weight of useful features while suppressing the weight of invalid features to improve detection accuracy. The experimental results show that the improved YOLOv4 has higher detection accuracy than the original YOLOv4, and has better detection results for small targets, multiple targets, and overlapping targets. The detection speed meets the real-time requirements, verifying the effectiveness of the improved algorithm.

scholarly journals DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 474
Author(s):  
Jun Wang ◽  
Liya Yu ◽  
Jing Yang ◽  
Hao Dong
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Detection Algorithms ◽  
Leaf Disease ◽  
Image Dataset

In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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