Detail Information Research Articles

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Abstract This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.

Core-attributes enhanced generative adversarial networks for robust image enhancement

Automated image enhancement algorithms have a profound impact on human life today. To solve the problems of luminance, lack of detail information, and overall color tone bias of images taken by mobile devices, a novel framework of core-attributes enhanced generative adversarial network (CAE-GAN) is designed to improve these core attributes of enhanced images. The generator in CAE-GAN mainly consists of a luminance correction encoder (LCE) and a high-frequency supplementary decoder (HFSD). To target the adaptive luminance improvement for each location, the encoder based on LCE is designed by combining the extracted prior knowledge of luminance. Meanwhile, a decoder based on HFSD is proposed to fill in missing edge details during the image reconstruction process. In addition, a multi-scale statistical characteristics distinction branch (MSCDB) is proposed to correct the overall tone. Moreover, an upgrade adversarial loss function is designed to focus on the discrimination of both multi-scale and multi-perspective. The generator and discriminator are iteratively trained under the constraints of the total loss function, resulting in the generator that automatically improves the visualization of the images. Extensive experiments have shown that our CAE-GAN is capable of achieving excellent results in several evaluation metrics and subjective results. The source code of the proposed CAE-GAN is available at https://github.com/SWU-CS-MediaLab/CAE-GAN.

LAND USE AND LAND COVER CHANGE AND ITS EFFECT ON LAND SURFACE TEMPERATURE: AN IMPACT ANALYSIS OF ROHINGYA INFLUX IN UKHIA UPAZILA BETWEEN YEAR 2000 TO 2020

Bangladesh is a country of near about 16 crore people with recently earning status of middle-income economy. Thecountry is already facing so many challenges in the economy and increasing population growth. Overcrowding is one ofthe main concerns of the country right now. Social Crimes, unsafety etc. are the day-to-day deals of the people of ourcountry. Though all these problems are present, the country is growing in the case of its economy. But a country like ourswhich is already encountering so many problems, is facing another big challenge of refugee issues. In 2017, 655,000Rohingya refugee came to Bangladesh in search of safety from the Myanmar militaries and built their residences at thecoastal upazilas of Bangladesh. This led to a tremendous amount of tree cutting, landfilling and other damage to nature.The Ukhia Upazila is the place for the largest refugee camp of the Rohingya people. To make this camp huge areas havefaced deforestation to meet the needs of the helpless people. Every element of nature has been damaged. By ConductingLand Use and Land Change Detection methods, the study has tried to show the amount of damage done by thesedeforestations and their impacts on the environmental parameter. From the years 2000 to 2020, a total of 36.39 sq. km.of vegetation area has been damaged. 25% of the bare lands have been converted to other land uses for fulfilling thepurposes of people living there. 2.88% of waterbodies have been decreased from the year 2010 to 2020. Dense vegetationhas decreased from 118.77 sq. km. to 82.3 sq. km. The land use change rate has also increased from 3.6% to 5.4% in overa year gap. The average land surface temperature increased to 26.5 degrees Celsius as a result of the continuouselimination of land covers. Further analysis of the thesis paper will give details information on the Land use landscapechange and its impact on the environment

Investigating The Effects of Twitter on Students’ English Writing Skill

With a limited number of characters per tweet, writing on Twitter requires creativity. It is unique media format has not only revolutionized online communication but also created a distinct style of writing. In this case, Twitter stands as a platform that reflects the way we write and communicate in English. Researcher wants to investigate the effects of twitter on students’ English writing skill. This is a case study method. This method helps the researcher to identify the problem in detail information. The data collected from questionnaire in English writing learning process. The lecturer becomes the observer or researcher of the research. On the other hand, students become the object of the research. As the conclusion, Twitter had a significant impact on students' writing skills in English. It challenges them to express their thoughts succinctly and effectively within a limited number of characters, encouraging creativity. However, it can also lead to errors and the use of informal language. Meanwhile, Twitter provides exposure to a variety of content and interactions with people from diverse backgrounds, which improves grammatical understanding. It also helps expand vocabulary by offering different words in context.

Underwater motion scene image restoration based on an improved U-Net network.

Active underwater polarization imaging is a common underwater imaging method, which uses the polarization difference between the reflected light and the scattered light in the underwater scene to suppress the scattered light, so as to improve the imaging quality of the underwater scene. However, the implementation often requires the acquisition of multiple polarization images, which is not suitable for the restoration of images of underwater motion scenes. To address the problem, a U-AD-Net deep learning network model based on a single polarized image is proposed, taking the polarization information of the single polarized image as the feature input, based on the classic U-Net network model, and introducing Dense-Net and spatial attention module. The learning ability and generalization ability of the proposed model for deep features are enhanced, and the polarization information that is most helpful to the image restoration is extracted, so as to restore the scene image more comprehensively. IE, AG, UCIQE, and SSIM are selected as evaluation metrics to assess the quality of the restored images. Experimental results show that the images restored through this proposed method contain richer detail information, having an obvious advantage to the existing network models. Since only a single polarized image is needed for restoration, this method has dynamic adaptability to underwater moving scene restoration.

Segmentation of wheat scab fungus spores based on CRF_ResUNet++

Accurate segmentation of tiny spore pictures is a significant technique in computer-aided evaluation of wheat scab. A CRF_ResUNet++ spore segmentation model using UNet++ as the fundamental framework is proposed to address the issues of uneven brightness, low spore-background contrast, and spore adhesion in microscopically collected Fusarium spore images. ResNet and fully connected conditional random fields (CRF) are combined in this model to address these issues. First, a residual block ResNet is introduced into UNet++ to enhance the propagation of features and extract more spore detail information. Next, the spore image is initially segmented using UNet++ with an encoder-decoder structure. Finally, postprocessing is carried out using a fully connected conditional random field model to obtain more precise edges and complete spore regions. The laboratory collection dataset test results revealed that the F1-score and MIoU reached 0.943 and 0.925, respectively, with an average detection Precision improvement of 3.2 % over the original UNet++ model. In comparison to existing models, the suggested model can segment spore pictures in challenging circumstances, such as when they contain independent and inter-adherent spores, with excellent computer vision processing capabilities effects and objective evaluation metric segmentation effects. In conclusion, the method in this study can reliably detect and segment adherent wheat scab spores in complex backgrounds, which offers critical technical support for automatic detection of adherent wheat scab spores and early spore concentration prediction in complex environments in the field.

A multi‐scale feature fusion convolutional neural network for facial expression recognition

AbstractThis paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

A fused super-resolution network and a vision transformer for airfoil ice accretion image prediction

Designing large neural network models targeting high accuracy and low sample number requirement is an important task in accelerating airfoil ice accretion calculation. Both convolutional neural network and Transformer are potential large-scale architectures for icing image generation. In this study, FSRN (fused super-resolution network) and ViT (Vision Transformer) are designed and applied to predict icing images with complex shapes. The novelty of FSRN lies in dividing the training process into main feature generation and detail information processing, and three branches of SR (super-resolution) networks are applied for multi-inputs and fused to generate icing images. The core part of ViT is the large-scale attention structure which is proven to be powerful in learning features. Both 7-dimensional icing condition vectors and the airfoil image are set as inputs to predict 136×136 icing images, and FocalLoss is chosen as the loss function. FSRN achieves determination coefficients of 0.974 and 0.958 respectively while ViT achieves 0.967 and 0.953 in two validation data sets. FSRN demonstrated greater potential in reducing sample number compared to ViT and MLP (multilayer perceptron).

Passable area segmentation for open-pit mine road from vehicle perspective

Recognition of passable areas of mine roads based on vehicle perspective is crucial for autonomous vehicles to drive in unmanned open-pit mine scenes. In the past few years, deep learning-based passable area recognition methods have been proven feasible. However, these works have mainly focused on structured urban road environments. Few works designed for mine road region detection due to dataset scarcity. In this article, we propose the mine road scapes segmentation dataset, which is collected from open-pit mine road environments. The dataset is collected from different mine areas, covers different daytime and dusk periods, and annotates the road and non-road areas in detail. Besides, a novel transformer-based road segmentation network is proposed, which is designed for the mine road characteristics of texture and distribution. The segmentation network is composed of an improved transformer feature extraction backbone, which can simultaneously aggregate global context and local detail information, and realize pixel-level recognition. We solve the problem of mine road segmentation benchmark under vehicle perspective. Moreover, by comparing the results of current mainstream methods on the mine road dataset, our method obtains state-of-the-art performance.

DFBU-Net: Double-branch flat bottom U-Net for efficient medical image segmentation

In the field of medical image processing, segmenting tissues and organs in CT/MRI and other medical sequence images is a vital yet challenging task. Analyzing the MICCAI competition, we have identified two problems in current methods for medical image organ segmentation: (1) There is a bottleneck in organ segmentation, with marginal room for improvement, as algorithmic capabilities have already surpassed the task’s inherent difficulty. (2) Most current research focuses on stacking and enhancing new modules for segmentation while overlooking the inherent characteristics of medical sequence images. To overcome these two problems, firstly, we have encapsulated the three characteristics of CT/MRI medical sequence image segmentation: semantic correctness, edge accuracy, and 3D structure. Secondly, we delved into the most information-rich downsampling stage in terms of detail and semantics. Subsequently, we designed a flat-bottom double-branch network (DFBU-Net) based on the U-Net architecture. The high-resolution flat bottom branch of this network maintained a 1/4 feature map size to ensure the preservation of rich detail information, while the low-resolution branch underwent progressive downsampling to capture more semantic information. To prevent information loss, cross-fusion was performed at each stage of the model’s two branches. Finally, DFBU-Net was evaluated on the MICCAI FLARE2021 dataset (DSC:93.61%, NSD:85.01%). Particularly, in the challenging task of pancreatic segmentation, our model outperformed the first-place model by 0.72% in DSC and 2.92% in NSD. Furthermore, in the MICCAI PARSE2022 competition, DFBU-Net ranked ninth with a DICE score of 79.28%, demonstrating its excellent segmentation performance and generalization ability.

St John’s Parish in Cape Town and a history of the lived spatial justice acts: 1956–2020

St John’s is an Anglican parish in the city of Cape Town. Its six member churches are in the southern suburbs of the city. In its history as a parish, it has gained land space in the southern suburbs of Cape Town. In recent years, numerous congregants and involved leaders began to think on how to use that land space in a way that brings justice in this area of the city. This article seeks to look at the parish’s historical spatial actions, in order to see what insights toward spatial justice may arise. This article provides detail information regarding the history of the Anglican Church and St John’s Parish. It also explores in detail the spatial knowledge and perspectives of some leaders and members of the parish. Finally, it explores what these interviews suggest for a church in search of spatial justice.Contribution: This article aligns with the scope of the journal as it created a conversation around church history, spatial history, and spatial justice within the South African context.

MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation

Combining color (RGB) images with thermal images can facilitate semantic segmentation of poorly lit urban scenes. However, for RGB-thermal (RGB-T) semantic segmentation, most existing models address cross-modal feature fusion by focusing only on exploring the samples while neglecting the connections between different samples. Additionally, although the importance of boundary, binary, and semantic information is considered in the decoding process, the differences and complementarities between different morphological features are usually neglected. In this paper, we propose a novel RGB-T semantic segmentation network, called MMSMCNet, based on modal memory fusion and morphological multiscale assistance to address the aforementioned problems. For this network, in the encoding part, we used SegFormer for feature extraction of bimodal inputs. Next, our modal memory sharing module implements staged learning and memory sharing of sample information across modal multiscales. Furthermore, we constructed a decoding union unit comprising three decoding units in a layer-by-layer progression that can extract two different morphological features according to the information category and realize the complementary utilization of multiscale cross-modal fusion information. Each unit contains a contour positioning module based on detail information, a skeleton positioning module with deep features as the primary input, and a morphological complementary module for mutual reinforcement of the first two types of information and construction of semantic information. Based on this, we constructed a new supervision strategy, that is, a multi-unit-based complementary supervision strategy. Extensive experiments using two standard datasets showed that MMSMCNet outperformed related state-of-the-art methods. The code is available at: https://github.com/2021nihao/MMSMCNet.

Image defocus deblurring method based on gradient difference of boundary neighborhood

BackgroundFor static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. MethodsTo this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. ResultsExperimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. ConclusionsExperimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

A pluggable single-image super-resolution algorithm based on second-order gradient loss

Convolutional neural networks for single-image super-resolution have been widely used with great success. However, most of these methods use L1 loss to guide network optimization, resulting in blurry restored images with sharp edges smoothed. This is because L1 loss limits the optimization goal of the network to the statistical average of all solutions within the solution space of that task. To go beyond the L1 loss, this paper designs an image super-resolution algorithm based on second-order gradient loss. We impose additional constraints by considering the high-order gradient level of the image so that the network can focus on the recovery of fine details such as texture during the learning process. This helps to alleviate the problem of restored image texture over-smoothing to some extent. During network training, we extract the second-order gradient map of the generated image and the target image of the network by minimizing the distance between them, this guides the network to pay attention to the high-frequency detail information in the image and generate a high-resolution image with clearer edge and texture. Besides, the proposed loss function has good embeddability and can be easily integrated with existing image super-resolution networks. Experimental results show that the second-order gradient loss can significantly improve both Learned Perceptual Image Patch Similarity (LPIPS) and Frechet Inception Distance score (FID) performance over other image super-resolution deep learning models.

Association between alcohol consumption and risk of hyperuricaemia among adults: a large cross-sectional study in Chongqing, China

ObjectiveTo investigate the relationship between alcohol consumption and hyperuricaemia (HUA), we conducted a study based on a large population.SettingCross-sectional study.ParticipantsA total of 20 833 participants aged 30–79 years were enrolled...

Adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure

In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field.

A Multi-Scale Recursive Attention Feature Fusion Network for Image Super-Resolution Reconstruction Algorithm

In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems with underutilization of feature information and loss of feature details. In this paper, a multi-scale recursive attention feature fusion network (MSRAFFN) is proposed for this purpose. The network consists of three parts: a shallow feature extraction module, a multi-scale recursive attention feature fusion module, and a reconstruction module. The shallow features of the image are first extracted by the shallow feature extraction module. Then, the feature information at different scales is extracted by the multi-scale recursive attention feature fusion network block (MSRAFFB) to enhance the channel features of the network through the attention mechanism and fully fuse the feature information at different scales in order to improve the network’s performance. In addition, the image features at different levels are integrated through cross-layer connections using residual connections. Finally, in the reconstruction module, the upsampling capability of the deconvolution module is used to enlarge the image while extracting its high-frequency information in order to obtain a sharper high-resolution image and achieve a better visual effect. Through extensive experiments on a benchmark dataset, the proposed network model is shown to have better performance than other models in terms of both subjective visual effects and objective evaluation metrics.

CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.

As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.

Scale-pyramid dynamic atrous convolution for pixel-level labeling

For achieving better performance, the majority of deep convolutional neural networks have endeavored to increase the model capacity by adding more convolutional layers or increasing the size of the filters. Consequently, the computational cost increases proportionally with the model capacity. This problem can be alleviated by dynamic convolution. In the case of pixel-level labeling, existing pixel-level dynamic convolution methods have a smaller scanning area than ordinary convolution or image-level dynamic convolution and are thus unable to exploit fine contextual information. As a consequence, pixel-level dynamic convolution is more sensitive to large-scale varying objects and confusion categories. In this paper, we propose a scale-pyramid dynamic atrous convolution (SDAConv) and exploit multi-scale pixel-level features in finer granularity, in order to efficiently increase model capacity, exploring contextual information, capture detail information and alleviate large-scale variation problem at the same time. Through kernel engineering (instead of network engineering), SDAConv dynamically arranges atrous filters in the individual convolutional kernels over different semantic areas at dense scales in the spatial dimension. By simply replacing the regular convolution with SDAConv in SOTA architectures, extensive experiments on three public datasets, Cityscapes, PASCAL VOC 2012 and ADE20K benchmarks demonstrate the superior performance of SDAConv on pixel-level labeling tasks.