New multi-resolution and multi-scale electromagnetic detection methods for urban underground spaces

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

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Oceanic Eddy Identification Using an AI Scheme

Remote Sensing ◽

10.3390/rs11111349 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1349 ◽

Cited By ~ 6

Author(s):

Guangjun Xu ◽

Cheng Cheng ◽

Wenxian Yang ◽

Wenhong Xie ◽

Lingmei Kong ◽

...

Keyword(s):

Sea Surface ◽

Detection Methods ◽

Small Scale ◽

Material Transport ◽

Multi Scale ◽

The Core ◽

Phytoplankton Distribution ◽

Oceanic Eddy ◽

Oceanic Eddies ◽

Eddy Detection

Oceanic eddies play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic eddies from sea surface height anomalies data. In order to adapt to segmentation problems for multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods. The results of eddies identified from this artificial intelligence (AI) method are well compared with those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of efforts to bridge AI techniques and oceanography research.

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SAR Ship Detection Based on Improved Libra RetinaNet

10.20944/preprints202108.0509.v1 ◽

2021 ◽

Author(s):

Haomiao Liu ◽

Haizhou Xu ◽

Lei Zhang ◽

Weigang Lu ◽

Fei Yang ◽

...

Keyword(s):

Feature Fusion ◽

Weather Conditions ◽

Maritime Transportation ◽

Detection Methods ◽

Sar Images ◽

Style Transfer ◽

Ship Detection ◽

Multi Scale ◽

Optical Images ◽

Average Accuracy

Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.

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A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds

10.5121/csit.2021.111812 ◽

2021 ◽

Author(s):

Hung-Hao Chen ◽

Chia-Hung Wang ◽

Hsueh-Wei Chen ◽

Pei-Yung Hsiao ◽

Li-Chen Fu ◽

...

Keyword(s):

Object Detection ◽

Receptive Fields ◽

Point Clouds ◽

Detection Methods ◽

Lidar Data ◽

3D Object ◽

Multi Scale ◽

Interest Level ◽

Rgb Images ◽

3D Object Detection

The current fusion-based methods transform LiDAR data into bird’s eye view (BEV) representations or 3D voxel, leading to information loss and heavy computation cost of 3D convolution. In contrast, we directly consume raw point clouds and perform fusion between two modalities. We employ the concept of region proposal network to generate proposals from two streams, respectively. In order to make two sensors compensate the weakness of each other, we utilize the calibration parameters to project proposals from one stream onto the other. With the proposed multi-scale feature aggregation module, we are able to combine the extracted regionof-interest-level (RoI-level) features of RGB stream from different receptive fields, resulting in fertilizing feature richness. Experiments on KITTI dataset show that our proposed network outperforms other fusion-based methods with meaningful improvements as compared to 3D object detection methods under challenging setting.

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Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery

Remote Sensing ◽

10.3390/rs11050531 ◽

2019 ◽

Vol 11 (5) ◽

pp. 531 ◽

Cited By ~ 34

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang ◽

Yingbo Dong ◽

Sisi Wei

Keyword(s):

Class Imbalance ◽

Weather Conditions ◽

Detection Methods ◽

Detection Accuracy ◽

Ship Detection ◽

Multi Scale ◽

Resolution Imaging ◽

Feature Pyramid ◽

Sar Imagery ◽

Ship Classification

Independent of daylight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect ships in marine surveillance. The shapes of ships are multi-scale in SAR imagery due to multi-resolution imaging modes and their various shapes. Conventional ship detection methods are highly dependent on the statistical models of sea clutter or the extracted features, and their robustness need to be strengthened. Being an automatic learning representation, the RetinaNet object detector, one kind of deep learning model, is proposed to crack this obstacle. Firstly, feature pyramid networks (FPN) are used to extract multi-scale features for both ship classification and location. Then, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. There are 86 scenes of Chinese Gaofen-3 Imagery at four resolutions, i.e., 3 m, 5 m, 8 m, and 10 m, used to evaluate our approach. Two Gaofen-3 images and one Constellation of Small Satellite for Mediterranean basin Observation (Cosmo-SkyMed) image are used to evaluate the robustness. The experimental results reveal that (1) RetinaNet not only can efficiently detect multi-scale ships but also has a high detection accuracy; (2) compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP). These results demonstrate the effectiveness of our proposed method.

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Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images

Sensors ◽

10.3390/s19235270 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5270

Author(s):

Yantian Wang ◽

Haifeng Li ◽

Peng Jia ◽

Guo Zhang ◽

Taoyang Wang ◽

...

Keyword(s):

Remote Sensing ◽

Detection Methods ◽

Computational Time ◽

Semantic Features ◽

Remote Sensing Images ◽

Bottom Level ◽

Multi Scale ◽

Noticeable Improvement ◽

Small Targets ◽

Aircraft Detection

Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method.

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BHi-Cect: a top-down algorithm for identifying the multi-scale hierarchical structure of chromosomes

Nucleic Acids Research ◽

10.1093/nar/gkaa004 ◽

2020 ◽

Vol 48 (5) ◽

pp. e26-e26

Author(s):

Vipin Kumar ◽

Simon Leclerc ◽

Yuichi Taniguchi

Keyword(s):

Hierarchical Structure ◽

Detection Methods ◽

Interaction Patterns ◽

Top Down ◽

Chromosome Conformation ◽

Multi Scale ◽

Topologically Associating Domains ◽

Genome Wide ◽

The Hierarchical Structure ◽

Respective Function

Abstract High-throughput chromosome conformation capture (Hi-C) technology enables the investigation of genome-wide interactions among chromosome loci. Current algorithms focus on topologically associating domains (TADs), that are contiguous clusters along the genome coordinate, to describe the hierarchical structure of chromosomes. However, high resolution Hi-C displays a variety of interaction patterns beyond what current TAD detection methods can capture. Here, we present BHi-Cect, a novel top-down algorithm that finds clusters by considering every locus with no assumption of genomic contiguity using spectral clustering. Our results reveal that the hierarchical structure of chromosome is organized as ‘enclaves’, which are complex interwoven clusters at both local and global scales. We show that the nesting of local clusters within global clusters characterizing enclaves, is associated with the epigenomic activity found on the underlying DNA. Furthermore, we show that the hierarchical nesting that links different enclaves integrates their respective function. BHi-Cect provides means to uncover the general principles guiding chromatin architecture.

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Multi-Scale Proposal Generation for Ship Detection in SAR Images

Remote Sensing ◽

10.3390/rs11050526 ◽

2019 ◽

Vol 11 (5) ◽

pp. 526 ◽

Cited By ~ 9

Author(s):

Nengyuan Liu ◽

Zongjie Cao ◽

Zongyong Cui ◽

Yiming Pi ◽

Sihang Dang

Keyword(s):

Area Under The Curve ◽

Detection Methods ◽

Edge Density ◽

Sar Images ◽

Ship Detection ◽

Multi Scale ◽

Hierarchical Grouping ◽

The Difference ◽

Two Stages ◽

Strong Scattering

The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.

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MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes

Sensors ◽

10.3390/s21155125 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5125

Author(s):

Pengcheng Xu ◽

Zhongyuan Guo ◽

Lei Liang ◽

Xiaohang Xu

Keyword(s):

Defect Detection ◽

Surface Defects ◽

Receptive Fields ◽

Feature Learning ◽

Learning Ability ◽

Detection Methods ◽

Feature Maps ◽

Scale Feature ◽

Learning Network ◽

Multi Scale

In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features.

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Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015400163 ◽

2015 ◽

Vol 24 (04) ◽

pp. 1540016 ◽

Cited By ~ 18

Author(s):

Muhammad Hussain ◽

Sahar Qasem ◽

George Bebis ◽

Ghulam Muhammad ◽

Hatim Aboalsamh ◽

...

Keyword(s):

Digital Image ◽

Image Data ◽

Feature Space ◽

Detection Methods ◽

Support Vector ◽

Data Sets ◽

Forgery Detection ◽

Data Set ◽

Multi Scale ◽

Copy Move Forgery Detection

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

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