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Enhancing lecture capture with deep learning

ADVANCES IN ENGINEERING SOFTWARE 2024年 196卷

作者： Sales, R. M. Giani, S. Univ Cambridge Whittle Lab Cambridge England Univ Durham Durham Coll Durham England

This paper provides an insight into the development of a state-of-the-art video processing system to address limitations within Durham University's 'Encore' lecture capture solution. The aim of the research described in this paper is to digitally remove the persons presenting from the view of a whiteboard to provide students with a more effective online learning experience. This work enlists a 'human entity detection module', which uses a remodelled version of the Fast Segmentation Neural Network to perform efficient binary image segmentation, and a 'background restoration module', which introduces a novel procedure to retain only background pixels in consecutive video frames. The segmentation network is trained from the outset with a Tversky loss function on a dataset of images extracted from various Tik-Tok dance videos. The most effective training techniques are described in detail, and it is found that these produce asymptotic convergence to within 5% of the final loss in only 40 training epochs. A cross-validation study then concludes that a Tversky parameter of 0.9 is optimal for balancing recall and precision in the context of this work. Finally, it is demonstrated that the system successfully removes the human form from the view of the whiteboard in a real lecture video. Whilst the system is believed to have the potential for real-time usage, it is not possible to prove this owing to hardware limitations. In the conclusions, wider application of this work is also suggested.

关键词： Convolutional neural network Semantic image segmentation Binary human segmentation learning rate optimisation Lecture capture technology

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Slim-neck by GSConv: a lightweight-design for real-time detector architectures

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JOURNAL OF real-time image processing 2024年第3期21卷 62-62页

作者： Li, Hulin Li, Jun Wei, Hanbing Liu, Zheng Zhan, Zhenfei Ren, Qiliang Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn Chongqing 400074 Peoples R China Univ British Columbia Okanagan Sch Engn Kelowna BC V1V 1V7 Canada

real-time object detection is significant for industrial and research fields. On edge devices, a giant model is difficult to achieve the real-time detecting requirement, and a lightweight model built from a large number of the depth-wise separable convolutional could not achieve the sufficient accuracy. We introduce a new lightweight convolutional technique, GSConv, to lighten the model but maintain the accuracy. The GSConv accomplishes an excellent trade-off between the accuracy and speed. Furthermore, we provide a design suggestion based on the GSConv, slim-neck (SNs), to achieve a higher computational cost-effectiveness of the real-time detectors. The effectiveness of the SNs was robustly demonstrated in over twenty sets comparative experiments. In particular, the real-time detectors of ameliorated by the SNs obtain the state-of-the-art (70.9% AP(50) for the SODA10M at a speed of similar to 100 FPS on a Tesla T4) compared with the baselines. Code is available at https://***/alanli1997/slim-neck-by-gsconv.

关键词： GSConv deep learning CNNs real-time detection Lightweight Edge computing

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real-time classification on oral ulcer images with residual network and image enhancement

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IET image processing 2022年第3期16卷 641-646页

作者： Guo, Jianbin Wang, Haolin Xue, Xingsi Li, Mengting Ma, Zhongxiong Fujian Med Univ Sch & Hosp Stomatol Fujian Key Lab Oral Dis Fuzhou Peoples R China Fujian Med Univ Sch & Hosp Stomatol Fujian Prov Engn Res Ctr Oral Biomat Fuzhou Peoples R China Fujian Med Univ Sch & Hosp Stomatol Stomatol Key Lab Fujian Coll & Univ Fuzhou Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou Fujian Peoples R China

With the advances of deep learning research in the past few years, healthcare and smart medicines have been significantly developed. Inspired by the wide application of deep learning in medical image classification and disease diagnosis, this paper further proposes a variant of the Residual Network framework to classify the oral ulcer images in real-time. In particular, image pre-processing and enhancement techniques are used to enrich the datasets and reduce model overfitting. Besides, the transfer learning is further introduced into the residual blocks to improve the classification accuracy, with the later layers trained from the labeled datasets. To validate the performance of authors' proposal, it is compared with other classic deep learning models with respect to the classification sensitivity, specificity, and accuracy. The experimental results show that authors' approach outperforms those classic classification networks when the oral ulcers are classified and diagnosed in real-time.

关键词： transfer learning disease diagnosis classification sensitivity medical image classification image classification residual blocks patient diagnosis classic classification networks oral ulcers pattern classification learning (artificial intelligence) feature extraction deep learning research deep learning (artificial intelligence) smart medicines image enhancement Residual Network framework classification accuracy time classification medical image processing enhancement techniques classic deep learning models oral ulcer images diseases

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DReLNet: abnormal human activity classification in theft surveillance video using reinforcement learning based fusion

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SIGNAL image AND VIDEO processing 2025年第3期19卷 1-20页

作者： Kale, Shubhangi Prabhakar Raghunathan, Shriram VIT Bhopal Univ Sch Comp Sci & Engn Bhopal 466114 Madhya Pradesh India MIT Acad Engn Sch Comp Engn Alandi 412105 Maharashtra India

The surge in illegal activities has underscored the critical role of surveillance cameras in monitoring and recording suspicious actions. However, the vast data generated by these systems poses a significant challenge in classifying abnormal activities. To address this, DReLNet is proposed for Abnormal Human Activity Classification in Theft Surveillance Video Using Reinforcement learning-Based Fusion. The approach begins by extracting frames from surveillance videos, followed by noise reduction using a Stacked Bilateral Median Filter. Keyframes are identified via Absolute Consecutive Histogram Difference, and features are extracted using Capsule Net, DenseNet 201, and ResNet-152 models. These features are then fused, and abnormal activities are classified using the deep reinforcement learning-assisted model, DReLNet. The proposed model achieves an accuracy of 99.55% on the Theft dataset, significantly outperforming existing models such as deepCNN, conv_LSTM, Bi_GRU and GRU. Higher accuracy enhances the model's overall performance by reducing the probability of incorrect classifications, which is critical for effective real-time surveillance systems.

关键词： Median filter Reinforcement learning Capsule net DenseNet 201 ResNet-152 Consecutive histogram difference

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A Conditional Diffusion Model With Fast Sampling Strategy for Remote Sensing image Super-Resolution

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Meng, Fanen Chen, Yijun Jing, Haoyu Zhang, Laifu Yan, Yiming Ren, Yingchao Wu, Sensen Feng, Tian Liu, Renyi Du, Zhenhong Zhejiang Univ Sch Earth Sci Zhejiang Prov Key Lab GIS Hangzhou 310058 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Engn Res Ctr Geomat Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Zhejiang Univ Sch Software Technol Ningbo Peoples R China

Conventional deep learning-based methods for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution (SR) outputs of these methods are yet to become sufficiently satisfactory in visual quality. Recent diffusion model-based generative deep learning models are capable to enhance the visual quality of output images, but this capability is limited due to their sampling efficiency. In this article, we propose FastDiffSR, an SRSISR method based on a conditional diffusion model. Specifically, we devise a novel sampling strategy to reduce the number of sampling steps required by the diffusion model while ensuring the sampling quality. Meanwhile, the residual image is adopted to reduce computational costs, demonstrating that integrating channel attention and spatial attention begets a further improvement in the visual quality of output images. Compared to the state-of-the-art (SOTA) convolutional neural network (CNN)-based, GAN-based, and Transformer-based SR methods, our FastDiffSR improves the learned perceptual image patch similarity (LPIPS) by 0.1-0.2 and achieves better visual results in some real-world scenes. Compared with existing diffusion-based SR methods, our FastDiffSR achieves significant improvements in pixel-level evaluation metric peak signal-noise ratio (PSNR) while having smaller model parameters and obtaining better SR results on Vaihingen data with faster inference time by 2.8-28 times, showing excellent generalization ability and time efficiency. Our code will be open source at https://***/Meng-333/FastDiffSR.

关键词： Diffusion models Transformers Superresolution Remote sensing Visualization Computational modeling Training Conditional diffusion model deep learning generative models remote sensing super-resolution (SR)

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real-time Predictability Analysis and Enhancement of deep-learning-Based Object Tracking 24

Real-Time Predictability Analysis and Enhancement of Deep-Le...

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6th International Conference on image processing and Machine Vision (IPMV)

作者： Zhang, Mingyang Van Beeck, Kristof Goedeme, Toon Katholieke Univ Leuven Dept Elect Engn PSI EAVISE Res Grp Leuven Belgium

ISBN: (纸本)9798400708473

While Siamese object tracking has witnessed significant advancements, its hard real-time behaviour on embedded devices remains inadequately addressed. In many application cases, an embedded implementation should not only have a minimal execution latency, but this latency should ideally also be having zero variance, i.e. predictable. To bridge this gap, we firstly analyse the real-time predictability of components of a state-of-the-art deep-learning-based object video object tracking system. Our detailed experiments indicate the superiority of FPGA implementations in terms of hard real-time behaviour, but unveil important time-predictability bottlenecks. Then, we craft a dedicated hardware accelerator specifically for the bottleneck. Our method seamlessly integrates advanced tracker features and improves greatly the tracker's speed and time-predictability on embedded systems. Implemented on a KV260 board, our quantized tracker demonstrates superior performance. These findings spotlight the immense promise of hardware acceleration in real-time object tracking and set a benchmark for forthcoming hardware-software co-design pursuits focused on achieving time-predictable object tracking.

关键词： Siamese network FPGA real-time system predictability SiamRPN plus

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Radio-opaque contrast agents for liver cancer targeting with KIM during radiation therapy (ROCK-RT): an observational feasibility study

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RADIATION ONCOLOGY 2024年第1期19卷 1-8页

作者： Plant, Natalie Mylonas, Adam Sengupta, Chandrima Nguyen, Doan Trang Silvester, Shona Pryor, David Greer, Peter Lee, Yoo Young (Dominique) Ramachandran, Prabhakar Seshadri, Venkatakrishnan Trada, Yuvnik Khor, Richard Wang, Tim Hardcastle, Nicholas Keall, Paul Univ Sydney Image Inst 10 Suite 201Biomed BldgC811 Cent Ave Eveleigh NSW 2015 Australia Princess Alexandra Hosp Dept Radiat Oncol Brisbane Qld Australia Calvary Mater Newcastle Dept Radiat Oncol Newcastle NSW Australia Austin Hlth Olivia Newton John Canc Wellness & Res Ctr Melbourne Vic Australia Crown Princess Mary Canc Ctr Dept Radiat Oncol Sydney NSW Australia Peter MacCallum Canc Ctr Melbourne Vic Australia

BackgroundThis observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular *** study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are <= 2 mm and <= 3 mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial *** a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial *** registrationRegistered to *** (NCT05169177) 12th October 2021.

关键词： Neoplasms Radiotherapy Kilovoltage Intrafraction Monitoring image Guidance Stereotactic ablative Radiation Therapy (SABR) Liver Radio-opaque contrast Transarterial chemoemobilisation (TACE)

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Research on Parking Space Detection and Prediction Model Based on CNN-LSTM

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IEEE ACCESS 2024年 12卷 30085-30100页

作者： Xu, Zhuye Tang, Xiao Ma, Changxi Zhang, Renshuai Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou 730070 Peoples R China Changsha Univ Sci & Technol Natl Engn Res Ctr Highway Maintenance Technol Changsha 410114 Hunan Peoples R China Lanzhou Jiaotong Univ Sch New Energy & Power Engn Intelligent Bldg & Bldg Automat Lab Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Transportat Lanzhou 730070 Peoples R China

With the continuous acceleration of urbanization, the parking problem is becoming increasingly serious. How to better manage parking resources has become an urgent problem to be solved in urban development. In this context, according to the historical data and real-time video data collected by the parking camera, this paper proposes an algorithm for parking space detection and state recognition. Through image preprocessing, region of interest selection, Hough line detection, and parking information recognition of the input test image, an intelligent parking space detection model is constructed, which improves the utilization rate of parking space and reduces the management cost. On this basis, according to the free parking space data obtained by the detection algorithm, a short-term demand prediction algorithm for on-road parking based on Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM) was proposed. Through the preprocessing of input parking space data, time vector transformation, data separation, model training, and prediction, the parking demand data is predicted and analyzed. By comparing the prediction results of multiple models, it was found that the CNN-LSTM prediction model had the best model stability and goodness of fit, the lowest Mean Error (MAE) and Root Mean Square Error (RMSE), the errors of working days were 13.301 and 21.156, and the errors of rest days were 12.573 and 20.739, respectively. It shows that CNN-LSTM can effectively capture the time and spatial feature information of parking lot free parking space data, and the prediction accuracy is good, which can be used to predict the number of free parking spaces in parking lots.

关键词： Automobiles Predictive models Convolutional neural networks real-time systems Gray-scale Urban areas image edge detection deep learning Key words deep learning parking space detection image processing convolutional neural network long short-term memory neural network

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PHL-YOLO: a real-time lightweight yarn inspection method

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JOURNAL OF real-time image processing 2025年第1期22卷 1-14页

作者： Dai, Jiachao Ren, Jia Li, Shangjie Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Changshan Res Inst Co Ltd Quzhou 324299 Peoples R China

To meet the real-time and high-precision detection requirements in actual yarn production, this paper introduces a novel lightweight yarn detection method named PHL-YOLO, an optimized version of the YOLOv8 algorithm. Initially, we developed the PEC2F module by fusing PConv (partial convolution) and EMA (efficient multi-scale attention) components to enhance the model's feature extraction capabilities while concurrently reducing computational demands and the number of parameters. Subsequently, we deployed the HWD (Haar wavelet downsampling) module for downsampling, which effectively diminishes image resolution without sacrificing the critical information necessary for precise detection. Additionally, to address the excessive parameters and computational complexity in the detection head of YOLOv8, we introduced the LSCDH (lightweight shared convolutional detection head) module, which significantly simplifies the structure of the detection head. Finally, we applied a structured pruning technique, GSPMD (group-level structured pruning method with DepGraph), to further refine the model. The experimental outcomes indicate that our enhanced model PHL-YOLO has achieved a 1.0% increase in precision, a 2.5% improvement in recall, and a 2.3% increase in mAP (mean average precision), along with a 26 FPS performance boost over the original model. Furthermore, our model's computational load and parameter count are only 29.6% and 10.2% of those in the original YOLOv8 model, respectively. PHL-YOLO not only effectively reduces model complexity, but also upholds high performance levels, providing a valuable reference for rapid and accurate yarn detection in real-world production scenarios.

关键词： Yarn inspection deep learning YOLOv8 Lightweight Model pruning

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deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2024年 117卷 103184-103184页

作者： Ylisiurua, Sampo Sipola, Annina Nieminen, Miika T. Brix, Mikael A. K. Univ Oulu Res Unit Hlth Sci & Technol Oulu 90220 Finland Oulu Univ Hosp Dept Diagnost Radiol Oulu 90220 Finland Univ Oulu Med Res Ctr Oulu 90220 Finland Oulu Univ Hosp Oulu 90220 Finland Oulu Univ Hosp Dept Dent Imaging Oulu 90220 Finland Univ Oulu Res Unit Oral Hlth Sci Oulu 90220 Finland

Purpose: The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods. Methods: Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses. Results: The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods. Conclusion: Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

关键词： Cone -beam computed tomography deep learning image quality

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