Preliminary Study on Image Captioning for Construction Hazards by Wen-Ta Hsiao, Wen-Der Yu, Tao-Ming Cheng, Alexey Bulgakov

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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.…”
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Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction by Wei Png Chua, Chien Chern Cheah

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A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems by Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun, Md Al‐Amin Bhuiyan

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Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images by Ziyan Chen, Yinhua Chen, Yandong Su, Nian Jiang, Siyi Wanggou, Xuejun Li

“…Clinical and VASARI related radiological information were selected for machine learning prediction model construction. And MRI images from axial, sagittal and coronal views of orientation were also used for deep learning craniotomy approach prediction model establishment and evaluation. …”
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Constructing segmentation method for wheat powdery mildew using deep learning by Hecang Zang, Hecang Zang, Congsheng Wang, Qing Zhao, Qing Zhao, Jie Zhang, Jie Zhang, Junmei Wang, Guoqing Zheng, Guoqing Zheng, Guoqiang Li, Guoqiang Li

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The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images by XU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen

“…Against the backdrop of the widespread application of UAV remote sensing and the maturity of deep learning technology, this paper constructs a high-precision UAV remote sensing image dataset for rice identification, which includes different growth stages of rice, different resolutions, and regions. …”
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Deep Learning-based Identification of Personal Protective Equipment in Construction Area by Mutia Fadhilla, Sapitri Sapitri, Rizky Wandri

“…This study proves that the application of YOLO-based deep learning technology can be an effective solution to improve compliance with PPE use and reduce the risk of work accidents in the construction sector. …”
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Detection Method for Safety Helmet Wearing on Construction Sites Based on UAV Images and YOLOv8 by Xin Jiao, Cheng Li, Xin Zhang, Jian Fan, Zhenwei Cai, Zhenglong Zhou, Ying Wang

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Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning by Zihan Wang, Jinjin Wu, Chenbei Li, Bing Wang, Qingxia Wu, Lan Li, Huijie Wang, Chao Tu, Jianhua Yin

“…Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. …”
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Construction of GAN‐RES and Its Application to Small Sample Fusulinid Fossil Recognition by Jiahui Xu, Yang Lu, Xu Xu

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Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management by Li-Wei Lung, Yu-Ren Wang, Yung-Sung Chen

“…This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. …”
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Construction of prognostic scoring model for ovarian cancer based on deep learning algorithm by Xiaolin Zhong, Hongyang Xiao, Weihong Lu, Jiayuan Chen, Fan Chao, Ruiqin Tu

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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Conclusion Our findings demonstrate that the fusion model, which integrates a convolutional neural network (CNN) with traditional machine learning and deep transfer learning techniques, can effectively differentiate between benign and malignant thyroid nodules through the analysis of ultrasound images. …”
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Long-Range Imaging through Scattering Media Using Deep Learning by Ying Jin, Cangtao Zhou, Wanjun Dai

“…However, most of the previous experiments used numbers or letters for close-range imaging, while objects in life are colorful. In this study, a new deep learning network, DesUNet, was constructed to image realistic objects at medium and long distances under sunlight through scattering media, and to realize object recognition. …”
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Cloud Removal in Full-disk Solar Images Using Deep Learning by Zhenhong Shang, Peng Du, Zhenping Qiang, Runxin Li

“…This paper proposes a novel deep learning-based approach for cloud removal and feature restoration in H α full-disk solar images. …”
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Deep learning-assisted thermal image analysis for assessment of cementitious composite flaws by Yoonseon Cha, Hyojeong Yun, Sanghyeon Cho, Wonseok Chung

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Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem, Abdulaziz I. Almulhim

“…Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). …”
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Effectiveness of Image Augmentation Techniques on Non-Protective Personal Equipment Detection Using YOLOv8 by Sungman Park, Jaejun Kim, Seunghyeon Wang, Juhyung Kim

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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…The framework is composed of two phases: “deep learning” and “quantitative analysis.” In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. …”
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