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Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms

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MEASUREMENT 2020年 159卷 107801-107801页

作者： Zhou, Yun Pei, Yilin Li, Ziwei Fang, Liang Zhao, Yu Yi, Weijian Hunan Univ Hunan Prov Key Lab Damage Diag Engn Struct Changsha 410082 Hunan Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China Hunan Univ Key Lab Green & Adv Civil Engn Mat & Applicat Tec Changsha 410082 Hunan Peoples R China Hunan Agr Univ Coll Water Resource & Civil Engn Changsha 410128 Hunan Peoples R China

Accurate information regarding the weight of vehicle loads plays a significant role in maintaining the structural health of bridges. However, the only method currently available for ascertaining the weight of loads is the bridge weigh-in-motion (BWIM) system, which is not widely used because of the high cost of the large device involved. There is therefore a need to develop an effective, low-cost technology to ascertain vehicle loads in relation to spatiotemporal load distribution on long-span bridges. This paper proposes a non-contact vehicle identification methodology to distinguish a vehicle from its load based on machine vision technology and deep learning algorithms. The vehicle information (i.e., type, weight, position, and motion trajectory, etc.) is conveniently obtained from a roadside monitoring surveillance camera, while the axle-weight distribution interval for nine classified vehicle types is obtained from the statistical information of 8402 delivery vehicles from which the relationship between a unique vehicle type and the corresponding weight information is established. Meanwhile, a dataset containing 8624 vehicle images was established for training the deep convolutional neural network (DCNN), where nine rough-grained vehicle classifications were contained in order to enhance the generalizability of the network. Optimization analysis was conducted to improve the network accuracy in vehicle types identification. The position of vehicles can also be effectively detected by a faster region-based convolutional neural network (Faster R-CNN), where the pre-trained DCNN with 98.17% vehicle types classification accuracy is employed as the co-shared network layer to enhance computation efficiency. Utilizing the object detection results from the Faster R-CNN and utilizing a Kalman filter, a vehicle in motion could also be simultaneously real-time tracked by the monitoring video, while a graphical user interface (GUI) incorporated into the video camera enabled

关键词： Bridge health monitoring Vehicle rough-grained classification Vehicle tracking Non-contact machine vision deep learning algorithms Automatic identification system

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Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2017年第10期55卷 1829-1848页

作者： Hatipoglu, Nuh Bilgin, Gokhan YTU Dept Comp Engn TR-34220 Istanbul Turkey YTU Signal & Image Proc Lab SIMPLAB Istanbul Turkey Yildiz Tech Univ Grad Sch Nat & Appl Sci TR-34220 Istanbul Turkey

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

关键词： Histopathological images deep learning algorithms Computer-aided diagnosis systems Segmentation Spatial relationships

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A systematic review of deep learning algorithms utilising electroencephalography signals for epileptic seizure detection

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2024年第3期46卷 228页

作者： Choudhary, Sunil Kumar Bera, Tushar Kanti Natl Inst Technol Dept Elect Engn Durgapur WB India Dr BC Roy Engn Coll Dept Elect Engn Durgapur WB India

Researchers are diligently endeavouring to integrate artificial intelligence (AI) into medical practice to harness the most recent breakthroughs in these fields. Early identification and accurate disease prediction are the primary goals of healthcare to administer the efficient preventative care at any disease or critical illness like epilepsy. Epilepsy is a condition that is marked by repeated and unpredictable seizure-activity. The difficulty of accurately predicting epileptic seizures has not been fully solved yet. Recently, AI tools have been utilised to help the doctors by providing the disease information extracted from the patient's datasets. This paper discusses the applications of deep learning algorithms (DLA) for epileptic seizure detection utilising electroencephalography (EEG) signals. The significant obstacles associated with accurately detecting automated epileptic seizures have also been studied using DLA in conjunction with EEG-data. It also analyses the advantages, challenges and limitations of the DLA applied for epileptic-seizure detection (ESD).

关键词： EEG signals classification deep learning DL machine learning ML epileptic seizures detection and diagnosis deep learning algorithms DLA epileptic seizure detection ESD

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Application of deep learning algorithms in the field of intelligent Chinese medicine

Application of deep learning algorithms in the field of inte...

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作者： Yaofeng Miao School of engineering Xi'an International University

The realization of intelligence in Chinese medicine is an inevitable result of technological development. Thousands of years of experience in the development of Chinese medicine in our country have left us precious spiritual wealth. How to achieve further development with the help of deep learning algorithms in the context of big data is a subject worthy of study. Based on the background of big data, this paper analyzes the application of deep learning algorithms in the field of intelligent Chinese medicine.

关键词： deep learning algorithms Traditional Chinese medicine Intelligence

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Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis

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Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2024年 190卷 110007页

作者： Ting-Wei Wang Ming-Sheng Hsu Wei-Kai Lee Hung-Chuan Pan Huai-Che Yang Cheng-Chia Lee Yu-Te Wu Institute of Biophotonics National Yang Ming Chiao Tung University 155 Sec. 2 Li-Nong St. Beitou Dist. Taipei 112304 Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Biophotonics National Yang Ming Chiao Tung University 155 Sec. 2 Li-Nong St. Beitou Dist. Taipei 112304 Taiwan. Department of Neurosurgery Taichung Veterans General Hospital Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan. National Yang Ming Chiao Tung University Brain Research Center Taiwan National Yang Ming Chiao Tung University College Medical Device Innovation and Translation Center Taiwan. Electronic address: ytwu@nycu.edu.tw.

BACKGROUND:Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS:We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS:Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS:The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these

关键词： Brain metastases deep learning algorithms Detection MRI images, segmentation Meta-analysis

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Assessing greenspace and cardiovascular health through deep-learning analysis of street-view imagery in a cohort of US children

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ENVIRONMENTAL RESEARCH 2025年 265卷 120459页

作者： Yi, Li Rifas-Shiman, Sheryl Jimenez, Marcia Pescador Lin, Pi-I Debby Suel, Esra Hystad, Perry Larkin, Andrew Hankey, Steve Zhang, Wenwen Klompmaker, Jochem Oken, Emily Hivert, Marie-France Aris, Izzuddin James, Peter Harvard Med Sch Dept Populat Med Div Chron Dis Res Lifecourse Boston MA USA Harvard Pilgrim Hlth Care Inst 401 Pk Ave4th Floor East Boston MA 02215 USA Boston Univ Sch Publ Hlth Dept Epidemiol Boston MA USA UCL Ctr Adv Spatial Anal London England Oregon State Univ Coll Publ Hlth & Human Sci Corvallis OR USA Virginia Tech Sch Publ & Int Affairs Blacksburg VA USA Rutgers State Univ Edward J Bloustein Sch Planning & Publ Policy New Brunswick NJ USA Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA USA Brigham & Womens Hosp Channing Div Network Med Dept Med Boston MA USA Harvard Med Sch Boston MA USA Harvard TH Chan Sch Publ Hlth Dept Nutr Boston MA USA Massachusetts Gen Hosp Diabet Unit Boston MA USA Univ Calif Sacramento Davis Sch Med Dept Publ Hlth Sci Sacramento CA USA

Background: Accurately capturing individuals' experiences with greenspace at ground-level can provide valuable insights into their impact on children's health. However, most previous research has relied on coarse satellite- based measurements. Methods: We utilized CVH and residential address data from Project Viva, a US-based pre-birth cohort, tracking participants from mid-childhood to late adolescence (2007-21). A deep learning segmentation algorithm was applied to street-view images across the US to estimate % street-view trees, grass, and other greenspace (flowers, field, and plants). Exposure estimates were derived by linking street-view greenspace metrics to 500m of participants' residences during mid-childhood, early and late adolescence. CVH scores (range 0-100;higher indicate better CVH) were calculated using the American Heart Association's Life's Essential 8 algorithm at these three time points, incorporating four biomedical components (body weight, blood lipids, blood glucose, blood pressure) and four behavioral components (diet, physical activity, nicotine exposure, sleep). Linear regression models were used to examine cross-sectional and cumulative associations between street-view greenspace metrics and CVH scores. Generalized estimating equations models were used to examine associations between street-view greenspace metrics and changes in CVH scores across three timepoints. All models were adjusted for individual and neighborhood-level confounders. Results: Adjusting for confounders, a one-SD increase in street-view trees within 500m of residence was cross- sectionally associated with a 1.92-point (95%CI: 0.50, 3.35) higher CVH score in late adolescence, but not mid-childhood or early adolescence. Longitudinally, street-view greenspace metrics at baseline (either mid- childhood or early adolescence) were not associated with changes in CVH scores at the same and all subsequent time points. Cumulative street-view greenspace metrics across the three tim

关键词： Life's essential 8 Green space Street-view imagery Children and adolescents deep learning algorithms Environmental epidemiology

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Urban cities heatwaves vulnerability and societal responses towards hazard zoning: Social media real-time based heatwave detection using deep learning

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SUSTAINABLE CITIES AND SOCIETY 2025年 125卷

作者： Soomro, Shan-e-hyder Boota, Muhammad Waseem Soomro, Gul-e-Zehra Soomro, Mairaj Hyder Alias Aamir Hu, Caihong Li, Yinghai Guo, Jiali Wahid, Junaid Abdul China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Henan Univ Coll Geog & Environm Sci Kaifeng 475004 Peoples R China Univ Wollongong Sch Civil Min & Environm Engn Northfields Ave Wollongong NSW 2522 Australia Zhengzhou Univ Coll Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

Study region: Pakistan. Study focus: Enhanced social media data accessibility facilitates the effective detection of disaster-associated information, exemplified by urban heatwaves. Social media elucidates human behavior and geographical trends in Sindh Province, Pakistan, amidst intensifying extreme heat events. The utilization of this data facilitates a comprehensive examination of heatwaves, thereby enhancing the real-time management and mitigation strategies within urban environments. Innovative insights: This study introduces an innovative framework for the real-time extraction and analysis of heatwave-related data from Facebook in areas impacted by disasters. This framework employs topic modeling and transfer learning for examining heatwave disaster dynamics. It extracts temperature and humidity information from both textual content and visual representation using state-of-the-art deep learning techniques and fuses these insights via decision-making processes. A fine-grained location corpus specific to urban heatwaves was developed using a named entity recognition (NER) model. The BERT-BiLSTM-CRF model was then utilized to extract heatwave data accurately. In the case of southeastern Pakistan, the extracted heatwave points aligned with 86 % of officially documented incidents, predominantly occurring near urban areas. This framework improves situational awareness and supports real-time spatiotemporal analyses of the urban heatwaves, thus providing a new approach for effective disaster management and response.

关键词： Urban heatwaves Social media (Facebook) Society participation deep learning algorithms BERT-BiLSTM-CRF model Named entity recognition (NER)

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deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach

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ENVIRONMENTAL RESEARCH 2025年 274卷 121349页

作者： Tong, Jiahui Lian, Xiaoqing Yan, Jingyan Peng, Shouxin Tan, Yuxuan Liang, Wei Chen, Zhongyang Zhang, Lanting Pan, Xiang Xiang, Hao Wuhan Univ Sch Publ Hlth Dept Global Hlth Wuhan Peoples R China Wuhan Univ Global Hlth Inst Sch Publ Hlth Wuhan Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Peoples R China Wulituo Hosp Beijing Shijingshan Dist Beijing Peoples R China Yangzhou Univ Sch Nursing Yangzhou Peoples R China Yangzhou Univ Sch Publ Hlth Yangzhou Peoples R China

Evidence linking greenspace exposure to metabolic syndrome (MetS) remains sparse and inconsistent. This exploratory study evaluate the relationship between green visibility index (GVI) and normalized difference vegetation index (NDVI) with MetS prevalence, and quantifies the potential reduction in MetS burden from increased greenspace exposure. Participants were selected from the baseline survey of the Wuhan Chronic Disease Cohort. Street-view imagry was procured within buffer zones ranging from 50 to 500-m surrounding participants' residences. GVI was extracted from street-view images using a convolutional neural network model trained on CityScapes, while the NDVI was ascertained from satellite remote sensing data. We employed generalized linear mixed-effects models to assess the associations between greenspace with the risk of MetS. Additionally, restricted cubic spline function was applied to generate exposure-response curve. Leveraging a counterfactual causal inference framework, we quantified the potential diminution in MetS cases consequent to an elevation in NDVI levels within Wuhan. Within the 150-m buffer zone, each 0.1-unit increase in GVI and NDVI corresponded to 13% and 31% decline in the odds of MetS in the fully adjusted regression models, respectively. A negative non-linear relationship between GVI and MetS was observed when the GVI level exceeded 0.209, while a negative linear association for NDVI when its level exceeded 0.299. Assuming causality, 74,183 cases of MetS can be avoided by achieving greenness threshold of NDVI, amounting for 8.16% of total MetS prevalence in 2019. Our findings offer a compelling justification for the integration of greening policies in initiatives aimed at promoting metabolic health.

关键词： Metabolic syndrome Greenspace Street-view imagery deep learning algorithms Disease burden

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Enhancing Lidar-Based 3D Classification Through an Improved deep learning Framework With Residual Connections

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IEEE ACCESS 2025年 13卷 42836-42849页

作者： Lin, Shih-Lin Wu, Jun-Yi Natl Changhua Univ Educ Grad Inst Vehicle Engn Changhua 50007 Taiwan

The rapid advancement of autonomous driving and autonomous vehicle technology has intensified the need for robust environment perception. In this work, we leverage automotive LiDAR point cloud data, which are inherently sparse, noisy, and unstructured, posing challenges to conventional classification methods. To address these difficulties, we adopt deep learning-based 3D neural networks and specifically enhance the VoxNet architecture. Through systematic adjustments-such as increasing convolutional kernel size and quantity, incorporating dropout and Batch Normalization layers, and integrating residual connections-we achieve substantial accuracy gains. In particular, our improved VoxNetVR5 model attains 97.92% accuracy, outperforming the baseline VoxNet by around 4-5 percentage points (from similar to 93% to similar to 98%). This enhancement is critical for safety-sensitive autonomous driving applications, as even modest accuracy improvements significantly reduce classification errors. Although the increased network complexity and added layers lead to longer training times, the robust performance justifies the computational cost. The proposed VoxNetVR5 architecture and its associated hyperparameter optimizations offer a powerful and efficient solution for processing large-scale, high-dimensional LiDAR data, paving the way for safer and more reliable vehicle perception systems.

关键词： Autonomous driving LiDAR point cloud data deep learning algorithms 3D neural network VoxNet

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deep learning for safety risk management in modular construction: Status, strengths, challenges, and future directions

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AUTOMATION IN CONSTRUCTION 2025年 169卷

作者： Junjia, Yin Alias, Aidi Hizami Haron, Nuzul Azam Abu Bakar, Nabilah Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia

Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024 in modular construction. It found that the six most popular DL algorithms in this area are "Convolutional Neural Network (CNN)," "Recurrent Neural Network (RNN)," "Generative Adversarial Network (GAN)," "Auto-Encoder (AE)," "deep Belief Network (DBN)" and "Transformer." However, in addition to each algorithm's limitations, problems like data constraints, talent gaps, and a lack of guidance frameworks also exist. To address these issues, three strategies are proposed. They are "establishing a multi-modal data sharing platform," "proposing a paradigm framework for the application of DL algorithms," and "constructing a compound construction talent training mechanism," which provide researchers with future references.

关键词： Safety risk Systematic review Modular construction deep learning algorithms Convolutional neural network Autoencoder Generative adversarial network

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