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

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.

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

Information ◽

10.3390/info12110474 ◽

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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Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

Remote Sensing ◽

10.3390/rs13142686 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2686

Author(s):

Di Wei ◽

Yuang Du ◽

Lan Du ◽

Lu Li

Keyword(s):

Feature Extraction ◽

Supervised Learning ◽

Target Detection ◽

Scene Recognition ◽

Attention Mechanism ◽

Sar Image ◽

Target Level ◽

Sar Images ◽

Training Samples ◽

Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

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Multivariate Anomaly Detection for Earth Observations: A Comparison of Algorithms and Feature Extraction Techniques

10.5194/esd-2016-51 ◽

2016 ◽

Cited By ~ 1

Author(s):

Milan Flach ◽

Fabian Gans ◽

Alexander Brenning ◽

Joachim Denzler ◽

Markus Reichstein ◽

...

Keyword(s):

Feature Extraction ◽

Anomaly Detection ◽

Data Streams ◽

Multivariate Data ◽

Detection Methods ◽

Earth System ◽

Earth System Science ◽

System Science ◽

Detection Algorithms ◽

Earth Observations

Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advance our understanding of e.g. vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of climatic extreme events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations. This artificial experiment is needed as there is no 'gold standard' for the identification of anomalies in real Earth observations. Our results show that a well chosen feature extraction step (e.g. subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify 3 detection algorithms (k-nearest neighbours mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

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Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection

BioMed Research International ◽

10.1155/2017/2049094 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 7

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.

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

Remote Sensing ◽

10.3390/rs10091415 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1415 ◽

Cited By ~ 11

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.

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Attention-Based Automated Feature Extraction for Malware Analysis

Sensors ◽

10.3390/s20102893 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2893 ◽

Cited By ~ 3

Author(s):

Sunoh Choi ◽

Jangseong Bae ◽

Changki Lee ◽

Youngsoo Kim ◽

Jonghyun Kim

Keyword(s):

Feature Extraction ◽

Short Term Memory ◽

Attention Mechanism ◽

Detection Methods ◽

Malware Analysis ◽

Feature Extraction Method ◽

Detection Model ◽

System Calls ◽

Long Short Term Memory ◽

Program Interface

Every day, hundreds of thousands of malicious files are created to exploit zero-day vulnerabilities. Existing pattern-based antivirus solutions face difficulties in coping with such a large number of new malicious files. To solve this problem, artificial intelligence (AI)-based malicious file detection methods have been proposed. However, even if we can detect malicious files with high accuracy using deep learning, it is difficult to identify why files are malicious. In this study, we propose a malicious file feature extraction method based on attention mechanism. First, by adapting the attention mechanism, we can identify application program interface (API) system calls that are more important than others for determining whether a file is malicious. Second, we confirm that this approach yields an accuracy that is approximately 12% and 5% higher than a conventional AI-based detection model using convolutional neural networks and skip-connected long short-term memory-based detection model, respectively.

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Study on Anomaly Detection Methods in Hyperspectral Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.631-632.631 ◽

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.

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SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Sensors ◽

10.3390/s21082657 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2657

Author(s):

Shuangshuang Li ◽

Wenming Cao

Keyword(s):

Feature Extraction ◽

Early Stage ◽

Text Detection ◽

Detection Methods ◽

Good Effect ◽

Natural Scenes ◽

Training Time ◽

Scene Text Detection ◽

Scene Text ◽

Curved Text

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-117-2016 ◽

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.

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Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

Remote Sensing ◽

10.3390/rs13112207 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2207

Author(s):

Fengcheng Ji ◽

Dongping Ming ◽

Beichen Zeng ◽

Jiawei Yu ◽

Yuanzhao Qing ◽

...

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Target Detection ◽

Input Image ◽

Majority Voting ◽

Detection Methods ◽

Remote Sensing Images ◽

Public And Private ◽

Detection Model ◽

Private Datasets

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.

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