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YOLOV9-CBM: An Improved Fire detection Algorithm Based on YOLOV9

IEEE ACCESS 2025年 13卷 19612-19623页

作者： Geng, Xin Han, Xiao Cao, Xianghong Su, Yixuan Shu, Dongxue Zhengzhou Univ Light Ind Coll Bldg Environm Engn Zhengzhou 450001 Henan Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. Secondly, the RepNCSPELAN4 module of the YOLOv9 backbone was replaced with the C3 module containing SE Attention to improve detection efficiency while guaranteeing accuracy. Besides, we transformed the multi-scale fusion network PANet in the baseline algorithm into a bidirectional feature network pyramid BiFPN to facilitate the bidirectional flow of features, enabling the algorithm to fuse information at different scales more effectively. Finally, instead of CIoU losses, we adopted MPDIoU losses in bounding box regression, which improved the accuracy of model regression and classification. Experimental results indicate that compared with YOLOV9, the recall rate of YOLOV9-CBM has increased by 7.6% and the mAP has risen by 3.8%. The revised model demonstrates good generalization performance and robustness. Code and dataset are at https://***/GengHan-123/***.

关键词： Accuracy Feature extraction Attention mechanisms YOLO Inference algorithms detection algorithms Deep learning Biological system modeling Forestry Computer architecture BiFPN C3 fire detection MPDIoU SE YOLOV9

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A Novel Community detection Algorithm Based on Node Propagation and Attraction

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IEEE ACCESS 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710600 Peoples R China Qinghai Inst Technol Sch Comp & Informat Sci Xining 810016 Peoples R China Xian Univ Sch Informat Engn Xian 710065 Peoples R China Xian Big Data Serv Ctr Xian 710007 Peoples R China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms.

关键词： Optimization detection algorithms Accuracy Partitioning algorithms Complex networks Measurement Autoencoders Prediction algorithms Organizations Mutual information community detection node propagation attraction

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Unsupervised Difference-Guided Adaptive Fusion Network for Change detection in PolSAR Images

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

作者： Yang, Zhifei Wu, Yan Li, Ming Hu, Xin Li, Zhikang Xidian Univ Sch Elect Engn Remote Sensing Image Proc & Fus Grp Xian 710071 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

It is challenging to accurately extract change features in polarimetric synthetic aperture radar (PolSAR) image change detection. Recently, many existing convolutional neural network (CNN)-based change detection algorithms show satisfactory performance, but error detection still exists in the acquired change features. In this article, we propose an unsupervised difference-guided adaptive fusion network (DGAFN), which contains three key modules, i.e., adaptive difference extraction module (ADEM), difference feature enhancement module (DFEM), and adaptive weighted fusion module (AWFM). First, ADEM extracts initial multiscale difference features through nonlocal attention mechanisms guided by bitemporal feature differences, which helps the network to capture the correlation and difference between bitemporal features more accurately. Second, in order to further refine multiscale difference features and reduce pseudo-changes, DFEM uses class activation maps (CAMs) and contextual feature extraction modules (CFEMs) to perceive more polarization contextual information in a larger scale range. Finally, in order to improve the recognition ability of change targets with different sizes, AWFM adaptively fuses enhanced multiscale difference features in spatial and channel dimensions by introducing attention mechanisms to obtain change features with multiscale complementary information. Comparative experimental results on five measured PolSAR datasets obtained by GaoFen-3 show that DGAFN is superior to other relevant advanced algorithms in change detection performance.

关键词： Feature extraction Adaptive systems Data mining Convolutional neural networks detection algorithms Remote sensing Fuses Adaptive multiscale feature fusion difference-guided feature extraction polarimetric synthetic aperture radar (PolSAR) image unsupervised change detection

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EGRN-YOLO: An Enhanced Multi-View Remote Sensing detection Algorithm for Onshore Wind Turbines Based on YOLOv7

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IEEE ACCESS 2025年 13卷 42457-42471页

作者： Xue, Renzheng Xu, Haiqiang Wu, Qianlong Qiqihar Univ Sch Comp & Control Engn Qiqihar 161000 Peoples R China Qiqihar Univ Heilongjiang Key Lab Big Data Network Secur Detect Qiqihar 161000 Peoples R China

Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. To address these challenges, this paper introduces an enhanced multi-view onshore wind turbine remote sensing detection algorithm for UAVs based on YOLOv7, termed EGRN-YOLO. Firstly, the lightweight network EfficientNetV2 is utilized as the feature extraction backbone to reduce the number of model parameters and computational load. Secondly, to further minimize the model parameters, the GhostSPPCSPC module is introduced to replace the original pyramid module SPPCSPC. To maintain a balance between accuracy and parameter efficiency, the CAM attention mechanism is integrated to create the GhostSPPCSPC_CAM module, which effectively expands the network's receptive field and enhances its deep learning capabilities. Subsequently, to improve the network's focus on small targets and reduce the redundancy of gradient information during neural network inference, the RepNCSPELAN4_KAN module is proposed and integrated into the neck network. Finally, the NWD loss function is introduced to replace the CIoU loss function, thereby improving the accuracy of positive and negative sample allocation for small targets. Experimental results on the self-constructed VOC_WIND_TURBINE dataset demonstrate that the improved EGRN-YOLO model, compared to the benchmark YOLOv7 model, achieves significant improvements of 1.7% in mean average precision (mAP), 3.3% in precision, 1.8% in recall, and 18.1% in inference time. These results indicate a well-balanced trade-off between high accuracy and model efficiency, effectively meeting the demands for identifying wind

关键词： Remote sensing Wind turbines Feature extraction Accuracy Computational modeling Convolution YOLO Wind energy Neck detection algorithms Remote sensing images target detection YOLOv7 attention mechanism wind turbine detection

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A Low Signal-to-Noise Ratio Infrared Small-Target detection Network

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 8643-8658页

作者： Li, Fenghong Rao, Peng Sun, Wen Su, Yueqi Chen, Xin Chinese Acad Sci Key Lab Intelligent Infrared Percept Shanghai 200083 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys Shanghai 200083 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Space-based infrared detection technology is critical to space situational awareness, playing a significant role in noncooperative space object detection, threat perception, and space target surveillance. As space-based infrared detection technology evolves, the primary challenge is detecting more distant objects and achieving high precision in the detection of space targets with lower signal-to-noise ratios (SNRs). Owing to the scarcity of space-based data, existing methods for infrared small target detection (IRSTD) focus on high-SNR terrestrial images and perform poorly with extremely low-SNR space targets. We propose a novel low SNR space-based IRSTD network. We present a trajectory encoding enhancement module that uses multiframe data to accumulate energy along the target's trajectory. It leverages multiframe temporal information, effectively enhancing the target while suppressing the background. This module can be integrated into most single-frame target detection networks. Additionally, we combine residual networks with global context aggregation to enhance the network's ability to extract features from small infrared targets. In the feature fusion phase, we propose a multiscale perception fusion module that expands the receptive field of shallow features and integrates multiscale information to accurately detect targets. We conduct extensive validation on real infrared space target datasets and semisimulated datasets, and our approach achieves the best performance. For targets with an SNR of 0.7, over 97% detection and fewer than 10(-6) false alarms are achieved.

关键词： Feature extraction Object detection Signal to noise ratio Deep-space communications Clutter Trajectory detection algorithms Accuracy Three-dimensional displays Target tracking Deep learning (DL) low signal-to-noise ratio (SNR) target detection space-based infrared detection spatial-temporal feature

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PFARN: Pyramid Fusion Attention and Refinement Network for Multiscale Ship detection in SAR Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9821-9836页

作者： Li, Ke Yu, Hang Li, Suqi Chen, Shanhu Wang, Bailu Naval Univ Engn Inst Elect Engn Wuhan 430033 Hubei Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

Synthetic aperture radar (SAR)-based ship detection is of great value to military surveillance and civil port management. Recently, motivated by the excellent performance of convolutional neural networks (CNNs) for object detection in computer vision, their application to ship detection on SAR images has been attracting attention gradually. However, different from optical images, the SAR images have their own unique characteristics, posing challenges to the direct application of CNNs. First of all, ships from the remote sensing perspective have special proportions, and ship objects in SAR images lack prior multiscale aspect ratio information, which is not conducive to multiscale ship detection. Second, coastal ships sticking to the land have blurred ship edges and positions, which greatly interferes with stable detection. Furthermore, scattered noise at the sea surface can be confused with the tiny ships, possibly causing false alarm. To address the aforementioned problems, this article proposes a novel detection algorithm named pyramid fusion attention and refinement network (PFARN) for SAR ship detection. First, a shape scale convolution is used to improve the focus and extraction of mutiscale-ship features. Second, the feature maps are fused with pyramid fusion attention, which is based on self-attention and Gaussian cross-attention, ensuring alignment of both the semantic and spatial information. Third, a more suitable central loss function is used for the detection head to ensure that the bounding box flexibly adapts to the size and location of the object. Experimental studies show that the proposed PFARN achieves 98.1% average precision (AP) for all and 57.4% AP for tiny ships in the SSDD datasets, and 94.2% AP for all and 68.0% AP for tiny ships in the HRSID datasets, respectively, which is satisfactory for the detection of multiscale ships.

关键词： Marine vehicles Feature extraction Radar polarimetry Noise Semantics Spatial resolution Remote sensing detection algorithms Convolution Data mining Attention mechanism box refine feature fusion multiscale ship detection synthetic aperture radar (SAR) images

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Channel-Selection-Based Temporal Convolutional Network for Patient-Specific Epileptic Seizure detection

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2025年第1期17卷 179-188页

作者： Wang, Guangming Lei, Xiyuan Li, Wen Lee, Won Hee Huang, Lianchi Zhu, Jialin Jia, Shanshan Wang, Dong Zheng, Yang Zhang, Hua Chen, Badong Wang, Gang Xi An Jiao Tong Univ Inst Biomed Engn Sch Life Sci & Technol Key Lab Biomed Informat EngnMinist Educ Xian 710049 Peoples R China Minist Civil Affairs Key Lab Neuroinformat & Rehabil Engn Xian 710049 Peoples R China Kyung Hee Univ Dept Software Convergence Yongin 17104 South Korea Xi An Jiao Tong Univ Affiliated Childrens Hosp Dept Pediat Neurol Xian 710003 Peoples R China Xi An Jiao Tong Univ Inst Engn & Med Interdisciplinary Studies Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Dept Neurosurg Xian 710061 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Since sudden and recurrent epileptic seizures seriously affect people's lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this article. First, electroencephalogram (EEG) recordings were segmented into 2-s intervals and features were extracted from both the time and frequency domains. Then, the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the TCN to capture inherent temporal dependencies of EEG signals and detect seizure events. Children Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in samplewise analysis, respectively. In eventwise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 s, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.

关键词： Electroencephalography Feature extraction Recording Databases Electrodes Convolutional neural networks detection algorithms Channel selection long-term EEG signal seizure detection time convolutional network (TCN)

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High-Performance and Low-Complexity Multitouch detection for Variable Ground States

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IEEE SENSORS JOURNAL 2025年第2期25卷 3138-3150页

作者： Sim, Doeun Byun, Younghoon Kim, Inseob Lee, Youngjoo Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea HiDeep Inc Seoul 13493 South Korea

Multitouch detection algorithms are crucial for precise interaction with touch screens. However, as existing multitouch detection algorithms only target the good ground mass (GGM) environment, performance drops sharply when the ground state is unstable, such as the low ground mass (LGM) environment. This article introduces an enhanced multitouch detection algorithm tailored for both ground environments, addressing deficiencies in recognizing large touch areas and precise coordinates. First, the center of thumb search (CTS) method combined with an adaptive valley point division (VPD) skip process for large circular touches, such as thumb touches, enables detection without excessive segmentation. Second, conditional VPD thresholding is designed to distinguish similar single and multitouch in LGM environment. This algorithm posed challenges that negatively impacted the detection performance in the GGM environment;however, these issues were addressed through the development of ground state classifier (GSC). At last, CTS algorithm facilitates the distinction of the center of a largesized thumb touch, enhancing the resolution in closely spaced touch scenarios by properly partitioning touch groups. Experimental results demonstrate significant improvements in accuracy and linearity. We have quantitatively confirmed substantial enhancements in performance from a user standpoint, achieving 96.07% in accuracy for the total dataset and x13.36 better in linearity. These innovations collectively advance the state of touch detection technology in challenging LGM environments, presenting a robust framework for future applications.

关键词： Thumb Accuracy detection algorithms Sensors Stationary state Classification algorithms Touch sensitive screens Performance evaluation Partitioning algorithms Capacitance Connected component analysis (CCA) multitouch detection overlap split touch-screen panel (TSP)

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Bilevel Hyperparameter Optimization and Neural Architecture Search for Enhanced Breast Cancer detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment

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IEEE ACCESS 2024年 12卷 63618-63628页

作者： Khan, Salabat Nosheen, Fariha Naqvi, Syed Shehryar Ali Jamil, Harun Faseeh, Muhammad Ali Khan, Murad Kim, Do-Hyeun COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Jeju Natl Univ Big Data Res Ctr Jeju 63243 South Korea Jeju Natl Univ Dept Elect Engn Jeju 63243 South Korea Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea

Breast cancer, a widespread malignancy predominantly affecting women aged 40 and above, presents a significant global health challenge with high mortality rates. The scarcity of medical data underscores the need for collaborative efforts among hospitals to enhance automated breast cancer detection. This research employs decentralized Federated Learning (FL) to facilitate cooperative learning across an interconnected smart hospital network, addressing data privacy, regulatory compliance, voluminous medical image data, and the necessity for distributed machine learning. Our innovative approach integrates Ant Colony Optimization (ACO) for hyperparameter fine-tuning and Neural Architecture Search (NAS) in a collaborative framework for smart hospitals linked with decentralized edge intelligent networks. This optimization strategy significantly improves the performance of our breast cancer detection system. Through a comprehensive experimental study (including diverse datasets), we classify Normal vs. Mass and Benign vs. Malignant regions in mammograms within a decentralized, federated collaborative learning environment. Empirical results consistently highlight the superiority of models trained using our method over individual hospital client-level training. Our method yielded significant improvements across evaluation measures: for Normal vs. Mass, achieving 92.6% sensitivity, 93.0% specificity, and 93.0% accuracy;for Benign vs. Malignant, achieving 89.6% sensitivity, 91.6% specificity, and 89.7% accuracy. Moreover, it has obtained a 6% and 5% increase in accuracy for Normal vs. Mass and Benign vs. Malignant cases, respectively, compared to the PSO-based HPO method. This evidence underscores the potential of collaborative approaches, emphasizing decentralized FL as a robust paradigm in medical research. The incorporation of ACO optimization reinforces the effectiveness of the proposed computer-aided diagnosis (CAD) system, marking a noteworthy advancement in the ongoing

关键词： Solid modeling Data models Cancer Breast cancer Hospitals Collaboration Training Hyperparameter optimization Metaheuristics detection algorithms neural architecture search metaheuristic ACO decentralized federated learning breast cancer detection

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A Double Self-Supervised Model for Pitting detection on Ball Screws

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IEEE ACCESS 2024年 12卷 49249-49260页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng Chen, Junfan Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Intelligent Sci & Technol Shanghai 200093 Peoples R China

Automatic detection of pitting on Ball Screw Drive (BSD) is essential to ensure normal production activities. However, the scarcity of defective samples and precisely labeled data poses a significant challenge. To address this, we propose an efficient double self-supervised model that operates at both the image and pixel levels, aiming to construct a high-performance model trained with defect-free data for detecting unknown defects in BSD images. By incorporating global and local information and extracting features at multiple hierarchical levels, the model's generalization performance is enhanced. The image-level self-supervised representation is first learned by classifying normal images from the PasteNoise, a data augmentation approach by pasting noise patches at random locations in normal images. Meanwhile, the pixel-level self-supervised representation is learned by segmenting the noise patch to locate abnormal regions. Then, we introduce a novel feature masking strategy in a masking and prediction task for accurate defect localization. In addition, we use Histogram of Oriented Gradients (HOG) features with local contrast normalization as prediction targets to capture local shapes and appearances to improve the robustness of the model. The proposed method achieves competitive receiver operating characteristic curves of 97.42 (image-level) and 94.57 (pixel-level) on the BSD dataset. In experiments on the MVTec AD, the proposed model shows good performance, indicating the broad adaptability of our approach.

关键词： Convergence of numerical methods Self-supervised learning Deep learning detection algorithms Histograms Mechanical products Noise measurement Feature extraction Data models Predictive models Location awareness Convolutional neural network defect detection deep learning histogram of oriented gradients self-supervised learning

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