Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
As one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network ...As one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network improved from yolo-v3 for the tiny traffic sign with high precision in real-time. First, a visual multi-scale attention module(MSAM), a light-weight yet effective module, is devised to fuse the multi-scale feature maps with channel weights and spatial masks. It increases the representation power of the network by emphasizing useful features and suppressing unnecessary ones. Second, we exploit effectively fine-grained features about tiny objects from the shallower layers through modifying backbone Darknet-53 and adding one prediction head to yolo-v3. Finally, a receptive field block is added into the neck of the network to broaden the receptive field. Experiments prove the effectiveness of our network in both quantitative and qualitative aspects. The m AP@0.5 of our network reaches 0.965 and its detection speed is55.56 FPS for 512 × 512 images on the challenging Tsinghua-Tencent 100 k(TT100 k) dataset.展开更多
Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention.However,due to interference occasioned by density,overlap,and coverage,the tiny object detection in re...Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention.However,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult problem.Therefore,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)model.TO–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images.TO–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing images.To verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation.展开更多
Micro and nanoscale particles have played crucial roles across diverse fields,from biomedical imaging and environmental processes to early disease diagnosis,influencing numerous scientific research and industrial appl...Micro and nanoscale particles have played crucial roles across diverse fields,from biomedical imaging and environmental processes to early disease diagnosis,influencing numerous scientific research and industrial applications.Their unique characteristics demand accurate detection,characterization,and identification.However,conventional spectroscopy and microscopy commonly used to characterize and identify tiny objects often involve bulky equipment and intricate,time-consuming sample preparation.Over the past two decades,optical micro-sensors have emerged as a promising sensor technology with their high sensitivity and compact configuration.However,their broad applicability is constrained by the requirement of surface binding for selective sensing and the difficulty in differentiating between various sensing targets,which limits their application in detecting targets in their native state or in complex biological samples.Developing label-free and immobilization-free sensing techniques that can directly detect target particles in complex solutions is crucial for overcoming the inherent limitations of current biosensors.In this study,we design and demonstrate an optofluidic,high throughput,ultra-sensitive optical microresonator sensor that can capture subtle acoustic signals,generated by tiny particles from the absorption of pulsed light energy,providing photoacoustic spectroscopy information for real-time,label-free detection and interrogation of particles and cells in their native solution environments across an extended sensing volume.Leveraging unique optical absorption of the targets,our technique can selectively detect and classify particles flowing through the sensor systems without the need for surface binding,even in a complex sample matrix,such as whole blood samples.We showcase the measurement of gold nanoparticles with diverse geometries and different species of red blood cells in the presence of other cellular elements and a wide variety of proteins.These particles are effectively identified and classified based on their photoacoustic fingerprint that captures particle shape,composition,molecule properties,and morphology features.This work opens up new avenues to achieve rapid,reliable,and high-throughput particle and cell identification in clinical and industrial applications,offering a valuable tool for understanding complex biological and environmental systems.展开更多
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
基金supported by the National Key R&D Program of China(Grant Nos.2018YFB2101100 and 2019YFB2101600)the National Natural Science Foundation of China(Grant No.62176016)+2 种基金the Guizhou Province Science and Technology Project:Research and Demonstration of Science and Technology Big Data Mining Technology Based on Knowledge Graph(Qiankehe[2021]General 382)the Training Program of the Major Research Plan of the National Natural Science Foundation of China(Grant No.92046015)the Beijing Natural Science Foundation Program and Scientific Research Key Program of Beijing Municipal Commission of Education(Grant No.KZ202010025047)。
文摘As one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network improved from yolo-v3 for the tiny traffic sign with high precision in real-time. First, a visual multi-scale attention module(MSAM), a light-weight yet effective module, is devised to fuse the multi-scale feature maps with channel weights and spatial masks. It increases the representation power of the network by emphasizing useful features and suppressing unnecessary ones. Second, we exploit effectively fine-grained features about tiny objects from the shallower layers through modifying backbone Darknet-53 and adding one prediction head to yolo-v3. Finally, a receptive field block is added into the neck of the network to broaden the receptive field. Experiments prove the effectiveness of our network in both quantitative and qualitative aspects. The m AP@0.5 of our network reaches 0.965 and its detection speed is55.56 FPS for 512 × 512 images on the challenging Tsinghua-Tencent 100 k(TT100 k) dataset.
基金funded by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022IRP04)the Sichuan Natural Resources Department Project(Grant NO.510201202076888)+3 种基金the Project of the Geological Exploration Management Department of the Ministry of Natural Resources(Grant NO.073320180876/2)the Key Research and Development Program of Guangxi(Guike-AB22035060)the National Natural Science Foundation of China(Grant No.42171291)the Chengdu University of Technology Postgraduate Innovative Cultivation Program:Tunnel Geothermal Disaster Susceptibility Evaluation in Sichuan-Tibet Railway Based on Deep Learning(CDUT2022BJCX015).
文摘Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention.However,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult problem.Therefore,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)model.TO–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images.TO–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing images.To verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation.
基金supported in part by the Chan Zuckerberg Initiative(CZI)and the AI for Health Institute(AIHealth)at Washington University in St.Louis.
文摘Micro and nanoscale particles have played crucial roles across diverse fields,from biomedical imaging and environmental processes to early disease diagnosis,influencing numerous scientific research and industrial applications.Their unique characteristics demand accurate detection,characterization,and identification.However,conventional spectroscopy and microscopy commonly used to characterize and identify tiny objects often involve bulky equipment and intricate,time-consuming sample preparation.Over the past two decades,optical micro-sensors have emerged as a promising sensor technology with their high sensitivity and compact configuration.However,their broad applicability is constrained by the requirement of surface binding for selective sensing and the difficulty in differentiating between various sensing targets,which limits their application in detecting targets in their native state or in complex biological samples.Developing label-free and immobilization-free sensing techniques that can directly detect target particles in complex solutions is crucial for overcoming the inherent limitations of current biosensors.In this study,we design and demonstrate an optofluidic,high throughput,ultra-sensitive optical microresonator sensor that can capture subtle acoustic signals,generated by tiny particles from the absorption of pulsed light energy,providing photoacoustic spectroscopy information for real-time,label-free detection and interrogation of particles and cells in their native solution environments across an extended sensing volume.Leveraging unique optical absorption of the targets,our technique can selectively detect and classify particles flowing through the sensor systems without the need for surface binding,even in a complex sample matrix,such as whole blood samples.We showcase the measurement of gold nanoparticles with diverse geometries and different species of red blood cells in the presence of other cellular elements and a wide variety of proteins.These particles are effectively identified and classified based on their photoacoustic fingerprint that captures particle shape,composition,molecule properties,and morphology features.This work opens up new avenues to achieve rapid,reliable,and high-throughput particle and cell identification in clinical and industrial applications,offering a valuable tool for understanding complex biological and environmental systems.
