检索结果-内蒙古大学图书馆

Random Interpolation Resize: A free image data augmentation method for object detection in industry

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Wan, Dahang Lu, Rongsheng Xu, Ting Shen, Siyuan Lang, Xianli Ren, Zhijie Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Hefei Univ Technol Prov Key Lab Measuring Theory & Precis Instrument Hefei 230009 Anhui Peoples R China

In the inspection task of industrial machine vision, insufficient data frequently occurs, and data enhancement can artificially incorporate the prior knowledge of human vision, expand the image data, and improve the model's performance, which has become the model's standard configuration. However, the majority of current data enhancement methods are intended for general scenes, and there are few image enhancement methods designed particularly for industrial object detection. Furthermore, many data enhancement methods require additional training time or annotation recalculation. This paper proposes a novel image data enhancement method, Random Interpolation Resize (RIR). We alter the interpolation method of the standard resize step in the preprocessing from fixed collocation to random selection in order to expand the image data and improve the model's generalization and detection capabilities. There are no online editing options for annotations, and no additional training time is available. Multiple data sets were tested using various target detection algorithms, resulting in a 0.45% to 5.6% enhancement in mAP0.5 and an increase in the detection effect.

关键词： Data augmentation Image interpolation Object detection Industry applications deep learning algorithm Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Mixed local channel attention for object detection

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期123卷

作者： Wan, Dahang Lu, Rongsheng Shen, Siyuan Xu, Ting Lang, Xianli Ren, Zhijie Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Hefei Univ Technol Anhui Prov Key Lab Measuring Theory & Precis Instr Hefei 230009 Peoples R China

Attention mechanism, one of the most extensively utilized components in computer vision, can assist neural networks in emphasizing significant elements and suppressing irrelevant ones. However, the vast majority of channel attention mechanisms only contain channel feature information and ignore spatial feature information, resulting in poor model representation effect or object detection performance, and the spatial attention modules were often complex and expensive. In order to strike a balance between performance and complexity, this paper proposes a lightweight Mixed Local Channel Attention (MLCA) module to improve the performance of the object detection network, and it can simultaneously incorporate both channel information and spatial information, as well as local information and global information to improve the expression effect of the network. On this basis, the MobileNet-Attention-YOLO(MAY) algorithm for comparing the performance of various attention modules is presented. On the Pascal VOC and SMID datasets, MLCA achieves a better balance between model representation efficacy, performance, and complexity than alternative attention techniques. Against the Squeeze-and-Excitation(SE) attention mechanism on the PASCAL VOC dataset and the Coordinate Attention(CA) method on the SIMD dataset, the mAP is enhanced by 1.0 % and 1.5 %, respectively.

关键词： Attention mechanism Local channel attention Object detection deep learning algorithm Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Optical frequency multiplication using residual network with random forest regression

引用

HELIYON 2024年第10期10卷 e30958页

作者： Zhang, Qi Han, Xu Fang, Xinyu Liu, Ming Ge, Ke Jiang, Hong Changchun Univ Technol Coll Elect & Elect Engn 2055 Yanan St Changchun 130012 Peoples R China

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

关键词： Frequency multiplication modulation deep learning algorithm Residual network Random forest regression

来源：评论

学校读者我要写书评

暂无评论

Review of CNN in aerial image processing

引用

The Journal of Photographic Science 2023年第1期71卷 1-13页

作者： Xinni Liu Kamarul Hawari Ghazali Fengrong Han Izzeldin Ibrahim Mohamed a School of Information Xi’an University of Finance and Economics Xi’an People’s Republic of China b Faculty of Electrical and Electronics Engineering University Malaysia Pahang Pekan Malaysia c School of Computer Baoji University of Arts and Sciences Baoji People’s Republic of China

ABSTRACTABSTRACTIn recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view image processing because of the high resolution and heaps of images taken. This paper aims to review neural networks specifically on the aerial view image by drones and to discuss the work principles and classic architectures of convolutional neural networks, its latest research trend and typical models along with target detection in object detection, image classification and semantic segmentation. In addition, this study also provided a specific application in the aerial image. Finally, the limitations of the convolutional network and expected future development trends were also discussed. Based on the findings, the deep learning algorithm was observed to provide high accuracy, it outperformed other generally image processing-based techniques.

关键词： Aerial image review image processing drone technology object detection image classification deep learning algorithm convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

引用

AUTISM RESEARCH 2021年第12期14卷 2512-2523页

作者： Gao, Kun Sun, Yue Niu, Sijie Wang, Li Univ N Carolina Dept Radiol Developing Brain Comp Lab 130 Mason Farm Rd Chapel Hill NC 27599 USA Univ N Carolina Biomed Res Imaging Ctr 130 Mason Farm Rd Chapel Hill NC 27599 USA Univ Jinan Sch Informat Sci & Engn 336 West Rd Nan Xinzhuang Jinan Peoples R China

Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2 years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI);however, most of these fMRI-based studies include subjects older than 5 years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24 months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. Lay Summary The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this

关键词： autism Spectrum disorder (ASD) deep learning algorithm early-stage status prediction infant structural MRI subject-specific autism attention

来源：评论

学校读者我要写书评

暂无评论

The Recognition algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention

引用

MICROMACHINES 2023年第2期14卷 462-462页

作者： Zhang, Jinsong Wei, Xinpeng Wang, Zhiliang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Univ Sch Mech & Engn Sci Shanghai 200444 Peoples R China

The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).

关键词： deep learning algorithm two-phase flow image pattern recognition attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Federated learning based privacy preserved English accent training ecosystem for people with Indian language accent

引用

ENTERTAINMENT COMPUTING 2023年第1期46卷

作者： Angurala, Mohit Khullar, Vikas Vellore Inst Technol Univ Chennai Tamil Nadu India Chitkara Univ Inst Engn & Technol Punjab India

Language refers to the system of communication used by a community, while accent refers to the distinct way in which an individual or group pronounces words or phrases within that language. This paper aims to design and develop a privacy-preserving federated learning-based intervention for individuals to identify a recommending system for the improvement of English accents. In methodology, the deep learning algorithm is trained and validated using the AccentDB dataset and further same model is implemented over Federated learning Ecosystem. Data in terms of identical and independent distribution is analyzed to ensure the productivity of the trained model in data constraint environments. The results section is focused to analyse the utilization of pro-posed implementations. Further, the trained model is applied to mobile and web applications for accessing levels and then training English accents to individuals. Similarly, to analyze incremental learning through federated learning, the implementation is analyzed on basis of parameters viz., accuracy, precision, recall, and loss. In the future, this proposed system could be embedded with various other applications for the overall enhancement of effective communication skills.

关键词： Language Federated learning deep learning algorithm AccentDB dataset Incremental learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid of deep recurrent network and long short term memory for rear-end collision detection in fog based internet of vehicles

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Almutairi, Mubarak S. Almutairi, Khalid Chiroma, Haruna Univ Hafr Al Batin Coll Comp Sci & Engn Hafar al Batin Saudi Arabia Univ Hafr Batin Appl Coll Mech Engn Technol Hafar Al Batin Saudi Arabia Univ Hafr Al Batin Appl Coll Comp Sci & Engn Technol Hafar al Batin Saudi Arabia

The development and use of intelligent transportation systems as an emerging trend in the application of computational intelligence within the concept of internet of vehicles (IoV) is attracting attention in the academia and industries. The computational intelligence algorithms such as the deep learning is playing a significant role in the IoV. The detection of rear end collision in the IoV is a critical aspect of the IoV because of safety. Previous research applied genetically optimized artificial neural network (ANN) (GA-ANN) for the detection of rear end collision in the IoV. However, GA-ANN has limitations such as stretching of the entire network leading to reduced flexibility, deteriorating performance as data increases to very large size, lack of generalization power resulting from over-fitting of the training data and calling for additional effort for regularization functions to monitor the model's complexity. In this research, as an alternative to GA-ANN for improving the accuracy of rear end collision detection in IoV, we propose a hybrid of deep Recurrent Neural Network (DRNN) and Long Short Term Memory (LSTM) (DRNN-LSTM) for rear end collision detection in the IoV. The scenario for the IoV is created and simulated to generate the dataset. The propose DRNN-LSTM is applied to detect the rear end collision in IoV. The performance of the proposed DRNN-LSTM is compared with the constituent algorithms of the DRNN-LSTM, ANN and GA-ANN. The results show that the proposed DRNN-LSTM detects rear end collision in IoV better than the GA-ANN, LSTM, DRNN and ANN. Thus, our propose approach has potential to improve safety, and effectiveness of the overall vehicle mobility in the IoV environment. Keywords: Collision Detection;Internet of Vehicles;Long Short Term Memory;deep Recurrent Neural Network;deep learning algorithm.

关键词： Collision detection Internet of Vehicles Long short term memory deep Recurrent Neural Network deep learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images

引用

TRANSLATIONAL ONCOLOGY 2021年第9期14卷 101161-101161页

作者： Ba, Wei Wang, Rui Yin, Guang Song, Zhigang Zou, Jinyi Zhong, Cheng Yang, Jingrun Yu, Guanzhen Yang, Hongyu Zhang, Litao Li, Chengxin Chinese Peoples Liberat Army Gen Hosp Dept Dermatol 28 Fuxing Rd Beijing 100853 Peoples R China Med Sch 28 Fuxing Rd Beijing 100853 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Pathol Beijing Peoples R China Lenovo Res Artificial Intelligence AI Lab Beijing Peoples R China Hosp Chengdu Univ Tradit Chinese Med Chengdu Sichuan Peoples R China St Vincent Evansville Med Ctr Evansville WA USA Tianjin Chang Zheng Hosp Dept Dermatol Tianjin Peoples R China

Background: deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs). Methods: The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists. Results: The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI]: 92.0%-98.2%) and 96.0% (95% CI: 94.2%-97.8%), respectively. At the operating point of sensitivity of 95.1%, the algorithm revealed a comparable specificity with 7 dermatopathologists (97.3% vs. 96.0%, P = 0.11). At the operating point of specificity of 96.0%, the algorithm also achieved a comparable sensitivity with 7 dermatopathologists (96.5% vs. 95.1%, P = 0.30). A more transparent and interpretable diagnosis could be generated by highlighting the regions of interest recognized by the algorithm in WSIs. Conclusion: The performance of the deep learning algorithm was on par with that of 7 expert dermatopathologists in interpreting WSIs with melanocytic lesions. By pre-screening the suspicious melanoma regions, it might serve as a supplemental diagnostic tool to improve working efficiency of pathologists.

关键词： Artificial intelligence deep learning algorithm Melanoma Nevus Whole-slide pathological images

来源：评论

学校读者我要写书评

暂无评论

Facial image inpainting for big data using an effective attention mechanism and a convolutional neural network

引用

FRONTIERS IN NEUROROBOTICS 2023年 16卷 1111621页

作者： Lu, Xiaoman Lu, Ran Zhao, Wenhao Ma, Erbin Northeastern Univ Coll Sci Dept Math Shenyang Liaoning Peoples R China

Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output image artifacts or fuzzy textures. Therefore, it is of practical significance to study how to effectively restore an incomplete facial image. In this study, we proposed a facial image inpainting method using a multistage generative adversarial network (GAN) and the global attention mechanism (GAM). For the overall network structure, we used the GAN as the main body, then we established skip connections to optimize the network structure, and used the encoder-decoder structure to better capture the semantic information of the missing part of a facial image. A local refinement network has been proposed to enhance the local restoration effect and to weaken the influence of unsatisfactory results. Moreover, GAM is added to the network to magnify the interactive features of the global dimension while reducing information dispersion, which is more suitable for restoring human facial information. Comparative experiments on CelebA and CelebA-HQ big datasets show that the proposed method generates realistic inpainting results in both regular and irregular masks and achieves peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as other evaluation indicators that illustrate the performance and efficiency of the proposed model.

关键词： big data artificial intelligence (AI) deep learning algorithm deep learning-based facial image inpainting generative adversarial network convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：