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Syflo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines

JOURNAL OF real-time image processing 2025年第1期22卷 1-15页

作者： Sankuri, Raja Sekhar Sristy, Nagesh Bhattu Karri, Sri Phani Krishna Natl Inst Technol Andhra Pradesh Dept Elect Engn Tadepalligudem India Natl Inst Technol Andhra Pradesh Dept Comp Sci & Engn Tadepalligudem India

Sustainable transmission of electrical energy to consumers across regions relies heavily on the integrity of power transmission lines and continuous monitoring of assets is crucial for maintaining system reliability. Unmanned aerial vehicles have revolutionized defect identification in real-time and accessibility, even in difficult-to-reach geographical landscapes, thereby improving image-based inspections. This work introduces semisupervised Yolo with focal loss function (SYFLo), a novel method that augments YOLO for real-time health monitoring of electric assets in power transmission lines. SYFLo integrates the focal loss function with semi-supervised learning to effectively address the lack of abundant labeled data, data imbalances and enhance detection accuracy. Additionally, it improves data generalizability across a wide range of images, ensuring robust performance despite varied image backgrounds. By leveraging YOLOv8, SYFLo significantly improves fault identification, achieving a detection accuracy of 96.5% and an FPS of 16.39. Experimental results demonstrate the impact of the proposed approach, highlighting its potential to enhance the reliability of power transmission line monitoring. These findings underscore the importance of integrating advanced deep learning techniques with innovative loss functions to address common challenges in real-time health monitoring systems.

关键词： deep learning Power grid inspection Defect detection Semi-supervised learning Focal loss function

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deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

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ASTRONOMY & ASTROPHYSICS 2025年 693卷

作者： Schirninger, C. Jarolim, R. Veronig, A. M. Kuckein, C. Karl Franzens Univ Graz Inst Phys Univ Pl 5 A-8010 Graz Austria Natl Ctr Atmospher Res High Altitude Observ 3080 Ctr Green Dr Boulder CO USA Karl Franzens Univ Graz Kanzelhohe Observ Solar & Environm Res Graz Austria Inst Astrofis Canarias IAC Via Lactea S-N E-38205 San Cristobal la Laguna Tenerife Spain Univ La Laguna Dept Astrofis E-38206 San Cristobal la Laguna Tenerife Spain Max Planck Inst Sonnensystemforsch Justus Von Liebig Weg 3 D-37077 Gottingen Germany

Context. Large aperture ground-based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, ground-based observations are inherently limited due to Earth's turbulent atmosphere, requiring image correction techniques. Aims. Recent post-image reconstruction techniques are based on using information from bursts of short-exposure images. Shortcomings of such approaches are the limited success, in case of stronger atmospheric seeing conditions, and computational demand. real-time post-image reconstruction is of high importance to enabling automatic processing pipelines and accelerating scientific research. In an attempt to overcome these limitations, we provide a deep learning approach to reconstruct an original image burst into a single high-resolution high-quality image in real time. Methods. We present a novel deep learning tool for image burst reconstruction based on image stacking methods. Here, an image burst of 100 short-exposure observations is reconstructed to obtain a single high-resolution image. Our approach builds on unpaired image-to-image translation. We trained our neural network with seeing degraded image bursts and used speckle reconstructed observations as a reference. With the unpaired image translation, we aim to achieve a better generalization and increased robustness in case of increased image degradations. Results. We demonstrate that our deep learning model has the ability to effectively reconstruct an image burst in real time with an average of 0.5 s of processing time while providing similar results to standard reconstruction methods. We evaluated the results on an independent test set consisting of high- and low-quality speckle reconstructions. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstruction methods show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image info

关键词： atmospheric effects techniques: image processing telescopes Sun: atmosphere Sun: photosphere

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Efficient NPU-GPU scheduling for real-time deep learning inference on mobile devices

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JOURNAL OF real-time image processing 2025年第2期22卷 1-13页

作者： Yu, Chengwu Wang, Meng Chen, Shan Wang, Wanqi Fang, Weiwei Chen, Yanming Xiong, Neal N. Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Hubei Engn Res Ctr Intelligent Detect & Identifica Wuhan 430205 Hubei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Sul Ross State Univ Dept Comp Sci & Math Alpine TX 79830 USA

As the need for on-device artificial intelligence (AI) has increased in recent years, mobile devices tend to be equipped with multiple heterogeneous processors, including CPU, GPU, and Neural processing Unit (NPU). While NPUs can offer low-cost and real-time AI processing capabilities for deep Neural Network (DNN) inference, its limited resources often lead to a trade-off between performance and accuracy, potentially resulting in a non-trivial accuracy drop. To address this problem, we propose a new NPU-GPU Scheduling (NGS) framework for DNN-based video analytics. The main challenge lies in determining when and how to execute inference on the NPU/GPU to satisfy the performance objectives. To make more precise scheduling decisions, we first propose a new image complexity assessment model to replace the existing normalized edge density metric. Then, we formulate the scheduling problem with the objective of maximizing inference accuracy under the given latency constraint, and introduce an adaptive solution based on dynamic programming to determine which frames should be processed on the GPU and when to exit from inference for each of them. Extensive experiments conducted on a real mobile device show that our NGS framework substantially outperforms other solutions, and achieves a close-to-oracle performance.

关键词： Edge computing DNN inference Heterogeneous processors Task scheduling Dynamic Programming

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SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Lee, Kyungsu Lee, Haeyun Hwang, Jae Youn Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Elect Engn & Comp Sci Interdisciplinary Studies Artificial Intelligence Daegu 42988 South Korea

deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on anew indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems.

关键词： deep learning Semi-supervised learning Efficient learning Scene recognition Indoor localization

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Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

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BMC BIOINFORMATICS 2024年第1期25卷 134页

作者： Dolezal, James M. Kochanny, Sara Dyer, Emma Ramesh, Siddhi Srisuwananukorn, Andrew Sacco, Matteo Howard, Frederick M. Li, Anran Mohan, Prajval Pearson, Alexander T. Univ Chicago Dept Med Sect Hematol Oncol Med Ctr Chicago IL 60637 USA Ohio State Univ Dept Internal Med Div Hematol Comprehens Canc Ctr Columbus OH USA Univ Chicago Dept Comp Sci Chicago IL USA

deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

关键词： Digital pathology Computational pathology Software toolkit Whole-slide imaging Explainable AI Self-supervised learning

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Bubble evolution identification method based on U-Net deep learning

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SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Gou, Yuxin Shi, Dongyan Wang, Jiuqiang Zhang, Haifeng Cui, Xiongwei Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Shipbldg Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

This study addresses the challenge of automated analysis of underwater bubble dynamics, as traditional methods for bubble detection often struggle with precision and real-time processing, requiring labour-intensive manual analysis or inefficient image processing techniques. To overcome these limitations, we propose a U-Net deep learning-based method for bubble segmentation and dynamic analysis, utilising high-speed photography (62420 fps) to capture bubble formation and dissipation. The proposed method achieves accurate bubble segmentation through deep feature extraction and upsampling using the U-Net model, with a proportional estimation method applied to calculate bubble size, expansion rate, and movement rate. Validation through manual measurements and CFD simulations confirms the method's reliability. The results demonstrate that the U-Net model enhances the efficiency and accuracy of bubble identification, providing a robust tool for studying underwater bubble dynamics in real-time.

关键词： U-net deep learning bubble evolution identification underwater electric spark experiment expansion and movement rates automated image segmentation

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A novel hybrid Bayesian-optimized CNN-SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-18页

作者： Arif, Abdul Rao, Ponugoti Gangadhara Prasad, Kalapala JNTUK Univ Coll Engn A Kakinada 533003 India Aditya Univ Dept Mech Engn Surampalem 533437 AP India JNTUK Dept Mech Engn Univ Coll Engn A Kakinada 533003 India

This study presents a novel hybrid Bayesian-optimized CNN-SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis. The hybrid deep learning model, achieves unprecedented accuracy in real-time surface roughness classification and prediction from image data of Inconel 716 superalloy machined surface, eliminating human intervention for feature extraction and selection procedures. The Bayesian optimization algorithm (BOA) was employed to fine-tune the CNN model's hyperparameters. The derived optimal hyperparameters were used to train the CNN model, which achieved a 92.37% classification accuracy on a test dataset. The classification performance was further enhanced by integrating an SVM classifier achieving an accuracy of 98.21%. Additionally, the model's performance was compared with five state-of-the-art deep learning architectures (AlexNet, ResNet50, VGGNet, Faster R-CNN ResNet50, and CNN), with the proposed model showing superior performance in terms of precision (98.02%), sensitivity (98.40%), and F1-score (98.20%). For surface roughness estimation, the hybrid model demonstrated a 96.89% accuracy in predicting roughness values, highlighting its potential for real-time in-process quality monitoring in industrial applications. These results indicate the proposed hybrid approach demonstrated substantially better performance than other popular object detection models. Hence, the presented work demonstrates an efficient end-to-end approach based on the in-process acquired machined surface image analysis by eliminating human intervention in the process of image processing, feature extraction, and feature selection for the real-time classification and prediction of the surface roughness process.

关键词： Surface roughness prediction image-based classification Regression deep learning Convolutional neural networks Hyperparameters Bayesian optimization Support vector machine

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Speed-Up DDPM for real-time Underwater image Enhancement

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3576-3588页

作者： Lu, Siqi Guan, Fengxu Zhang, Hanyu Lai, Haitao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Heilongjiang Peoples R China

Underwater images often suffer from serious color bias and blurred features because of the effect of the water bodies on the light. To enhance underwater images, we present SU-DDPM, a method of real-time underwater image enhancement (UIE) based on a denoising diffusion probabilistic model (DDPM). SU-DDPM outperforms other baseline and generative adversarial network models in underwater image enhancement, thus establishing a new state-of-the-art baseline. SU-DDPM processes images more rapidly than the diffusion model, which makes it competitive with other deep learning-based methods. We demonstrate that if conditional DDPM is used directly for the UIE task, the processing speed is slow, and the enhanced images are of poor quality and show color bias. The quality of the enhanced image is improved by combining the degraded image with the reference image in the diffusion stage to create a fusion-DDPM model. The specificity of the UIE task allows us to accelerate the inference process by changing the initial sampling distribution and reducing the number of iterations in the denoising stage of the model. We evaluate SU-DDPM on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compare it to state-of-the-art models. SU-DDPM ensures increased enhancement quality, and enhancement processing speed is comparable to the speed of real-time enhancement models.

关键词： Underwater image enhancement denoising diffusion probabilistic model (DDPM) underwater image restoration deep learning

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A generic deep learning architecture optimization method for edge device based on start-up latency reduction

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JOURNAL OF real-time image processing 2024年第4期21卷 116-116页

作者： Li, Qi Li, Hengyi Meng, Lin Ritsumeikan Univ Coll Sci & Engn Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Res Org Sci & Technol Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Dept Elect & Comp Engn Kusatsu Shiga 5258577 Japan

In the promising Artificial Intelligence of Things technology, deep learning algorithms are implemented on edge devices to process data locally. However, high-performance deep learning algorithms are accompanied by increased computation and parameter storage costs, leading to difficulties in implementing huge deep learning algorithms on memory and power constrained edge devices, such as smartphones and drones. Thus various compression methods are proposed, such as channel pruning. According to the analysis of low-level operations on edge devices, existing channel pruning methods have limited effect on latency optimization. Due to data processing operations, the pruned residual blocks still result in significant latency, which hinders real-time processing of CNNs on edge devices. Hence, we propose a generic deep learning architecture optimization method to achieve further acceleration on edge devices. The network is optimized in two stages, Global Constraint and Start-up Latency Reduction, and pruning of both channels and residual blocks is achieved. Optimized networks are evaluated on desktop CPU, FPGA, ARM CPU, and PULP platforms. The experimental results show that the latency is reduced by up to 70.40%, which is 13.63% higher than only applying channel pruning and achieving real-time processing in the edge device.

关键词： Neural network compression Edge device PULP platform FPGA

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Energy-efficient real-time visual image adversarial generation and processing algorithm for new energy vehicles

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JOURNAL OF real-time image processing 2024年第5期21卷 1-13页

作者： Li, Yinghuan Liu, Jicheng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

With the rapid development of deep learning in the last decade, generating and processing real-time images have become one of critical methods in intelligent driving systems for new energy vehicles. However, the real-time images captured by sensors are susceptible to variations in various environments, including different weather and lighting conditions. To enhance the real-time image generation performance for new energy vehicles in complex environments, and improve real-time visual image processing capabilities, this study proposes an energy-efficient real-time visual image adversarial generation and processing algorithm, called as ENV-GAN. It hypothesizes a shared latent domain among mixed image domains after analyzing driving situations under various weather and lighting conditions. Mappings are established between different image domains. Besides, a multi-encoder weight-sharing technique is utilized to enhances the generative adversarial network model. Additionally, the algorithm integrates an attention module to enhance the model & acirc;(TM) s image generation. Experimental results and analysis demonstrate that the new algorithm outperforms existing algorithms in tasks such as defogging, rain removal, and lighting enhancement, offering high energy efficiency and low energy consumption.

关键词： New energy vehicles Generative adversarial network image processing Intelligent driving Energy efficiency

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