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FCDNet: A Lightweight Network for Real-Time Wildfire Core detection in Drone Thermal Imaging

IEEE ACCESS 2025年 13卷 14516-14530页

作者： Wang, Linfeng Doukhi, Oualid Lee, Deok Jin Jeonbuk Natl Univ Ctr Autonomous Intelligence & E Mobil Dept Mech Design Engn Jeonju Si 54896 South Korea

The increasing number of wildfires damage nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection. This paper introduces a novel infrared wildfire detection network named FCDNet to tackle this issue. It includes an Efficient Processing (EP) module based on the novel Partial Depthwise Convolution (PDWConv) and the lightweight feature-sharing decoupled detection head (Fast Head), achieving low-size and low-computation wildfire detection. An Adaptive Sample Attention (ASA) Loss is introduced to enhance the detection accuracy of wildfire cores in combination with Normalized Wasserstein Distance (NWD) Loss. The experiment shows that the model size of FCDNet is only 4.0MB, representing 63.5% of the baseline YOLOv8n network, with 63.3% of its parameters. It operates at just 5 Giga Floating Point Operations Per Second (GFLOPs), 38.3% lower, and achieves a 77.5% mAP (@50-95 IOU), a 1% increase, with a 460x460 input image size. Compared to the state-of-the-art YOLOv11n, FCDNet reduces parameters, computation, and model size by 26.9%, 20.6%, and 27.3%, respectively. The thermal dataset and training codes used in this study are made publicly available at: https://***/WangLF1996/FCDNet-Dataset-and-Algorithm

关键词： Wildfires Accuracy Real-time systems Drones Head Computational modeling detection algorithms Cameras Feature extraction Autonomous aerial vehicles FCDNet infrared wildfire detection lightweight network YOLOv8n-based

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Autonomous Aerial Vehicle Object detection Based on Spatial Perception and Multiscale Semantic and Detail Feature Fusion

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IEEE ACCESS 2025年 13卷 42897-42909页

作者： Rao, Wei Chen, Siyuan Li, Dan NanChang Inst Technol Nanchang 330099 Peoples R China Fudan Univ Res Ctr Smart Networks & Syst Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China

Owing to changes in the spatial position of Autonomous Aerial Vehicle (AAV) aerial images and limited platform resources, most existing AAV aerial image detection models have low accuracy, and it is difficult to achieve a good balance between detection performance and lightweight. To solve the above problems, an object detection model of AAV aerial images based on YOLOv8s, called BSDS-YOLOv8s, is proposed. In the proposed model the Dynamic Head (DyHead) is used to replace the detection head of YOLOv8s firstly, which can improve the spatial perception ability of the detection head of the model. Second, to improve the detection performance of DyHead and make the model lightweight, a new feature pyramid network (SDI-MBiFPN) in the Neck of YOLOv8s is proposed, which contains a Multiscale Bidirectional Feature Pyramid Network (BiFPN), called MBiFPN, and a feature fusion method based on the redesigned Semantic and Detail Fusion (SDI). Finally, a method that integrates Soft Non-Maximum Suppression (Soft-NMS) with Generalized Intersection over Union (GIoU), called GIoU-Soft-NMS, is proposed to enhance the model's post-processing capability and reduce the missed and false detection rates, thereby further improving the detection accuracy of the model. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of BSDS-YOLOv8s reached 49.6% and 34.1%, respectively, on the VisDrone2019 dataset. This is an improvement of 9.2 and 9.8 percentage points over YOLOv8s, respectively. The number of parameters and the floating point operations (FLOPs) were 8.03M and 26.6G, 27.8% and 7.6% less than those of YOLOv8s, respectively.

关键词： Feature extraction Autonomous aerial vehicles Computational modeling Accuracy Head Neck detection algorithms Convolution YOLO Semantics YOLOv8s Autonomous Aerial Vehicles (AAVs) MBiFPN SDI DyHead GIoU Soft-NMS

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Novel Chewing Cycle Approach for Peak detection Algorithm of Chew Count Estimation

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IEEE SENSORS JOURNAL 2025年第1期25卷 803-812页

作者： Asmiza Selamat, Nur Hamid Md Ali, Sawal Ghadafi Ismail, Ahmad Anom Ahmad, Siti Nisa' Minhad, Khairun Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tekn Malaysia Melaka Fak Teknol Kejuruteraan Elekt Durian Tunggal 76100 Melaka Malaysia Univ Kebangsaan Malaysia Inst Microengn & Nanoelect IMEN Bangi 43600 Selangor Malaysia Univ Putra Malaysia UPM Fac Engn Serdang 43400 Selangor Malaysia Xiamen Univ Malaysia Dept Elect & Elect Engn Sepang 43900 Selangor Malaysia

Chew count is a critical parameter in analyzing mastication signals, yet traditional methods of manual counting by trained clinicians are often labor-intensive and prone to errors. As a result, there has been a growing interest among researchers in developing automated methods for estimating chew count. This article reviews the existing approaches, evaluates their effectiveness, and proposes a new approach based on optimization technique. This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. The chewing dataset comprises signals collected from 20 participants consuming eight different food types, with proximity sensors (PSs) detecting temporalis muscle activity. The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. Two specific chewing cycle approaches were implemented: a participants-based (P) cycle and a participants-food type-based (PF) cycle. These approaches were compared to the traditional All (A) chewing cycle method, which evaluates chew count across the entire dataset in a single analysis. Results demonstrate that the PF method yields the lowest MAE at 1.25%, followed by the P method at 3.46%, and the A method at 4.26%. Moreover, the PF method required the least computational time at 8012.2 s, compared to 9392.0 s for the P method and 36621.4 s for the A.

关键词： Reviews Masticatory muscles Estimation Manuals Muscles Robustness Sensors detection algorithms Particle swarm optimization Optimization Chew count estimation chewing cycle chewing episode maximum peak width minimum peak prominence particle swarm optimization (PSO) peak detection

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Distributed MIMO Radar Target detection in Multipath Clutter Environments With Time Reversal

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4907-4925页

作者： Lian, Hao Yang, Minglei Zhou, Dingsen Liu, Meng Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The distributed multiple-input-multiple-output (MIMO) radar, as an emerging active sensing technology, uniquely leverages spatial diversity to significantly enhance radar detection performance. The time reversal (TR) technology can be incorporated into radar systems to further improve detection performance by effectively utilizing multipath signals. However, the coexistence of multipath signals and clutter presents a formidable challenge to radar detection, potentially diminishing its performance. To tackle this challenge, we propose a distributed MIMO radar detection algorithm that integrates the TR technology with a power allocation (PA) approach tailored for multipath clutter environments. Specifically, the algorithm initially leverages TR technology to efficiently process and exploit multipath signals, while concurrently optimizing the distribution of radar transmit power through strategic allocation, thereby bolstering detection performance in complex environments. On this foundation, a distributed MIMO radar signal model is established, and the TR-PA-MIMO detector is derived, embodying the proposed solution. Furthermore, a conventional distributed MIMO (CD-MIMO) detector is developed as a benchmark for comparative analysis. Several other detection algorithms are also evaluated under identical conditions to assess their relative performance. The Monte Carlo simulations and theoretical analysis demonstrate that the proposed detection algorithm outperforms all other algorithms for distributed MIMO radar systems operating in multipath clutter environments.

关键词： MIMO radar Clutter Radar Detectors Transmitting antennas Object detection Radar detection Radar antennas detection algorithms Resource management distributed MIMO radar target detection time reversal (TR)

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Microwave Technologies in Experiments for detection of Dark Matter Axions

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IEEE MICROWAVE MAGAZINE 2025年第3期26卷 78-87页

作者： Navarro-Madrid, Jose R. Garcia-Barcelo, Jose Maria Diaz-Morcillo, Alejandro Univ Politecn Cartagena Dept Informat Technol & Commun Cartagena 30202 Spain Max Planck Inst Phys & Astrophys Werner Heisenberg Inst D-85748 Garching Germany

One of the major challenges of modern physics is the detection of dark matter and the study of its composition. For this purpose, there are several experiments where microwave technology is present. In this work, we describe experiments for the detection of dark matter axions and the most important design features that microwave devices must have for this application.

关键词： Microwave technology Superconducting materials Three-dimensional displays Dark matter Superconducting magnets Superconducting filters Superconducting device noise Microwave filters Superconducting microwave devices detection algorithms

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

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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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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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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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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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