Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple...Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.展开更多
With the rapid development of wearable electronics,flexible pressure sensors have attracted wide attention in human–computer interaction and intelligent machines.However,it is a challenge to achieve a sensor with hig...With the rapid development of wearable electronics,flexible pressure sensors have attracted wide attention in human–computer interaction and intelligent machines.However,it is a challenge to achieve a sensor with high sensitivity,wide measurement range,and wearing comfortability.Here,we develop an oriented electrospinning thermoplastic polyurethane/polyacrylonitrile(TPU/PAN)nanofibers(OETPN)based piezoresistive pressure sensor(PONPS)in which the active layer and the electrode are assembled perpendicularly.The interdigital electrode is fabricated by spraying silver nanowires(AgNWs)on the OETPN through a mask plate.The active layer is composed of OETPN coated with MXene,encapsulated on the electrode by polyurethane(PU)film.The porous structure of nanofibers membrane broadens the measurement range of the sensor.Employing oriented nanofibers as active layer can improve the sensitivity in low pressure,because oriented nanofibers without interweaving nanofibers are more compressible than disordered nanofibers.Electrode prepared using the spraying method creates nanoscale microstructure and increases sensitivity.The perpendicular assembly has greater response between the active layer and the electrode than the parallel assembly to improve the sensitivity.The sensor exhibits high sensitivity(6.71 kPa^(−1),0.02–2 kPa)and wide measurement range(0.02–700 kPa).The sensor can detect weak signals such as radial artery.A pressure array constructed precisely represents the distribution of pressure.An intelligent throat is created by combining machine learning algorithms with the PONPS.It can detect and recognize subtle throat vibrations while speaking,achieving recognition accuracy up to 100%using support vector machine(SVM)for five words with different syllables.The fabricated sensor shows promising prospects in personal healthcare and intelligent robots.展开更多
基金supported by the National Natural Science Foundation of China(No.62203163)the Scientific Research Project of Hunan Provincial Education Department(No.24A0519)+1 种基金the Hunan Provincial Natural Science Foundation(No.2025JJ60407)the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX2024100).
文摘Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.
基金supported by the National Natural Science Foundation of China(Nos.52175554,52205608,and 62171414)the Fundamental Research Program of Shanxi Province(Nos.20210302123059 and 20210302124610)the program for the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2020L0316).
文摘With the rapid development of wearable electronics,flexible pressure sensors have attracted wide attention in human–computer interaction and intelligent machines.However,it is a challenge to achieve a sensor with high sensitivity,wide measurement range,and wearing comfortability.Here,we develop an oriented electrospinning thermoplastic polyurethane/polyacrylonitrile(TPU/PAN)nanofibers(OETPN)based piezoresistive pressure sensor(PONPS)in which the active layer and the electrode are assembled perpendicularly.The interdigital electrode is fabricated by spraying silver nanowires(AgNWs)on the OETPN through a mask plate.The active layer is composed of OETPN coated with MXene,encapsulated on the electrode by polyurethane(PU)film.The porous structure of nanofibers membrane broadens the measurement range of the sensor.Employing oriented nanofibers as active layer can improve the sensitivity in low pressure,because oriented nanofibers without interweaving nanofibers are more compressible than disordered nanofibers.Electrode prepared using the spraying method creates nanoscale microstructure and increases sensitivity.The perpendicular assembly has greater response between the active layer and the electrode than the parallel assembly to improve the sensitivity.The sensor exhibits high sensitivity(6.71 kPa^(−1),0.02–2 kPa)and wide measurement range(0.02–700 kPa).The sensor can detect weak signals such as radial artery.A pressure array constructed precisely represents the distribution of pressure.An intelligent throat is created by combining machine learning algorithms with the PONPS.It can detect and recognize subtle throat vibrations while speaking,achieving recognition accuracy up to 100%using support vector machine(SVM)for five words with different syllables.The fabricated sensor shows promising prospects in personal healthcare and intelligent robots.
