Federated learning based fire detection method using local MobileNet

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 5, 2024

Fire is a dangerous disaster that causes human, ecological, and financial ramifications. Forest fires have increased significantly in recent years due to natural artificial climatic factors. Therefore, accurate early prediction of essential. While significant advancements been made traditional Deep Learning (DL) methods for fire detection, challenges remain accurately pinpointing recognizing regions, especially diverse large environments, prevent damage effectively. To address these challenges, this paper introduces novel Federated (FL)-based method called Indoor-Outdoor FireNet (IOFireNet) detecting localizing regions. The proposed incorporates Bilateral Filter (BF) effectively preprocess images reduce noise artifacts enhance detection clarity. It employs Super Pixel-based Adaptive Clustering (SPAC) precisely segment non-fire A global IOFireNet model developed aggregate parameters from local models, improving accuracy across varied while MobileNet used efficient data processing, enabling predictions on spread, severity, affected areas support warnings. FL-based attains an rate 98.65% 97.14% mean IoU segmentation. SPAC reaches 4.06%, which 2.45% better than the graph cut algorithm CRF model. achieves 0.23%, 4.20%, 3.29%, 10.02%, VGG-19, ResNet-50, Inception, Dense Net, respectively.

Language: Английский

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Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea Clutter

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2613 - 2613

Published: April 20, 2025

Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to dynamic and unpredictable nature maritime environments. However, conventional CFAR detectors suffer significant performance degradation multi-target scenarios, primarily masking effect caused by interfering targets. To address this challenge, paper introduces an advanced detection scheme that integrates Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) processing. Lin-DBSCAN specifically tailored efficiently identify isolate targets spikes, which typically manifest as outliers symmetric reference windows surrounding Cell Under Test (CUT). By leveraging Lin-DBSCAN, proposed Lin-DBSCAN-CFAR method effectively filters out anomalous signals from background clutter, resulting enhanced accuracy robustness, especially under complex conditions. Extensive simulations varying conditions, including multiple target environments, false alarm rates, different shape parameters, demonstrate significantly outperforms approaches. It noteworthy achieves comparable more computationally intensive DBSCAN-CFAR while reducing computational complexity. Simulation results reveal requires 1 2 dB lower SNR reach probability 0.8 compared nearest traditional techniques, confirming its superiority both efficiency.

Language: Английский

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A Noisy Sample Selection Framework Based on a Mixup Loss and Recalibration Strategy

Mathematics, Journal Year: 2024, Volume and Issue: 12(15), P. 2389 - 2389

Published: July 31, 2024

Deep neural networks (DNNs) have achieved breakthrough progress in various fields, largely owing to the support of large-scale datasets with manually annotated labels. However, obtaining such is costly and time-consuming, making high-quality annotation a challenging task. In this work, we propose an improved noisy sample selection method, termed “sample framework”, based on mixup loss recalibration strategy (SMR). This framework enhances robustness generalization abilities models. First, introduce robust function pre-train two models identical structures separately. approach avoids additional hyperparameter adjustments reduces need for prior knowledge noise types. Additionally, use Gaussian Mixture Model (GMM) divide entire training set into labeled unlabeled subsets, followed by using semi-supervised learning (SSL) techniques. Furthermore, cross-entropy (CE) prevent from converging local optima during SSL process, thus further improving performance. Ablation experiments CIFAR-10 50% symmetric 40% asymmetric demonstrate that modules introduced paper improve accuracy baseline (i.e., DivideMix) 1.5% 0.5%, respectively. Moreover, experimental results multiple benchmark our proposed method effectively mitigates impact labels significantly performance DNNs datasets. For instance, WebVision dataset, improves top-1 0.7% 2.4% compared method.

Language: Английский

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Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network

Sensors, Journal Year: 2024, Volume and Issue: 24(19), P. 6405 - 6405

Published: Oct. 2, 2024

High-quality video object segmentation is a challenging visual computing task. Interactive can improve results. This paper proposes multi-round interactive dynamic propagation instance-level network based on click interaction. The consists of two parts: user interaction module and bidirectional module. A prior was designed in the to better segment objects different scales that users on. achieves high-precision through fusion masks obtained from multiple rounds Experiments datasets show our method state-of-the-art results with fewer interactions.

Language: Английский

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Improved Generalized-Pinball-Loss-Based Laplacian Twin Support Vector Machine for Data Classification

Symmetry, Journal Year: 2024, Volume and Issue: 16(10), P. 1373 - 1373

Published: Oct. 15, 2024

Nowadays, unlabeled data are abundant, while supervised learning struggles with this challenge as it relies solely on labeled data, which costly and time-consuming to acquire. Additionally, real-world often suffer from label noise, degrades the performance of models. Semi-supervised addresses these issues by using both data. This study extends twin support vector machine generalized pinball loss function (GPin-TSVM) into a semi-supervised framework incorporating graph-based methods. The assumption is that connected points should share similar labels, mechanisms handle noisy labels. Laplacian regularization ensures uniform information spread across graph, promoting balanced assignment. By leveraging term, two quadratic programming problems formulated, resulting in LapGPin-TSVM. Our proposed model reduces impact noise improves classification accuracy. Experimental results UCI benchmarks image demonstrate its effectiveness. Furthermore, addition accuracy, also measured Matthews Correlation Coefficient (MCC) score, experiments analyzed through statistical

Language: Английский

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An improved sample selection framework for learning with noisy labels

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0309841 - e0309841

Published: Dec. 5, 2024

Deep neural networks have powerful memory capabilities, yet they frequently suffer from overfitting to noisy labels, leading a decline in classification and generalization performance. To address this issue, sample selection methods that filter out potentially clean labels been proposed. However, there is significant gap size between the filtered, possibly subset unlabeled subset, which becomes particularly pronounced at high-noise rates. Consequently, results underutilizing label-free samples methods, leaving room for performance improvement. This study introduces an enhanced framework with oversampling strategy (SOS) overcome limitation. leverages valuable information contained instances enhance model by combining SOS state-of-the-art methods. We validate effectiveness of through extensive experiments conducted on both synthetic datasets real-world such as CIFAR, WebVision, Clothing1M. The source code will be made available https://github.com/LanXiaoPang613/SOS.

Language: Английский

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