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.

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.

scholarly journals A Fast Orientation Invariant Detector Based on the One-stage Method

2018 ◽  
Vol 232 ◽  
pp. 04036
Author(s):  
Jun Yin ◽  
Huadong Pan ◽  
Hui Su ◽  
Zhonggeng Liu ◽  
Zhirong Peng
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
High Efficiency ◽  
Detection Method ◽  
State Of The Art ◽  
Orientation Angle ◽  
Detection Methods ◽  
Detection Algorithms ◽  
Bounding Boxes ◽  
The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

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 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.

scholarly journals DBA_SSD ： A Novel End-to-End Object Detection Using Deep Attention Module for Helping Smart Device with Vegetable and Fruit Leaf Plant Disease Detection

2021 ◽  
Author(s):  
Jun Wang ◽  
Jing Yang ◽  
Liya Yu ◽  
Hao Dong ◽  
Kun Yun ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Smart Device ◽  
Training Time ◽  
Pests And Diseases ◽  
Detection Algorithms ◽  
Image Dataset ◽  
Pest Detection

Abstract In response to the difficulty of detecting and classifying pests and vegetable and fruit leaves with pests and diseases, this study proposes a novel vegetable and fruit leaf pest detection method called deep block attention SSD (DBA_SSD) for the identification of pests and diseases and classification of the degree of pests and diseases of vegetable and fruit leaves. We propose three vegetable and fruit leaf pest detection methods, namely, squeeze-and excitation SSD (Se_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 vegetable and fruit disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. 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.

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.

HUMAN DETECTION IN VIDEO AND IMAGES — A STATE-OF-THE-ART SURVEY

2014 ◽  
Vol 28 (03) ◽  
pp. 1455004 ◽  
Author(s):  
GURJIT SINGH WALIA ◽  
RAJIV KAPOOR
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Intelligent Vehicles ◽  
Human Detection ◽  
Detection Methods ◽  
Qualitative And Quantitative ◽  
Survey Paper ◽  
Detection Algorithms ◽  
Static Images ◽  
And Robotics

The aim of this paper is to give an overview of state-of-the-art human detection methods, classify them into reasonable categories and listing new trends in this field. Human detection in static images or video sequences is a challenging area of research with its main application in surveillance, defence, intelligent vehicles and robotics. In this survey paper, we classify the human detection methods based upon the mode of data acquisition and features used for detection of human. The review of different algorithms reported in last few years for human detection is tabulated. Also, we reviewed standard dataset for evaluation of human detection and studied the statistics about these dataset such as number of frames, challenging environments conditions, ground truth structure, camera details, etc. Different performance measures for human detection algorithms both qualitative and quantitative are listed out. Our survey gauges the gap between the present research and future requirements.

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