Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
植被侵蚀是导致长城结构坍塌的重要潜在因素,相关检测研究主要包括人工巡检与自动化模型。前者现场勘查效率低下;后者受限于小目标漏检与计算冗余,难以实现精准高效的监测。因此,提出一种基于YOLOv11网络架构改进的长城植被侵蚀智能检...植被侵蚀是导致长城结构坍塌的重要潜在因素,相关检测研究主要包括人工巡检与自动化模型。前者现场勘查效率低下;后者受限于小目标漏检与计算冗余,难以实现精准高效的监测。因此,提出一种基于YOLOv11网络架构改进的长城植被侵蚀智能检测算法GWVE-YOLO(the Great Wall vegetation erosion,you only look once)。通过新增P2尺度小目标检测层,增强高分辨率浅层特征提取能力,显著降低微小病害目标的漏检率;在主干与颈部浅层引入RCS-OSA(reparameterized convolution based on channel shuffle of one-shot aggregation)模块,通过通道重排与结构重参数化技术优化特征融合效率,在维持精度的同时降低计算冗余;采用动态非单调聚焦机制的W-IoU(wise intersection over union)v3损失函数,自适应调整低质量样本梯度权重,以提升复杂背景下小目标定位精度。在自建无人机长城病害数据集,经实验表明,所提方法的m AP50达到78.8%、精确率达到79.8%,每秒帧数114,综合性能优于YOLO系列及其已有改进等主流检测模型。展开更多
Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limit...Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.展开更多
Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classificati...Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.展开更多
采摘机器人或无人机等智能农业装备在进行视觉检测作业时往往背景环境复杂、画面中同时存在大量样本,而且现有目标检测方法对低质量样本考虑不充分,致使传统目标检测模型检测不准。该文提出一种基于YOLOv11-MW的轻量化农作物实时检测模...采摘机器人或无人机等智能农业装备在进行视觉检测作业时往往背景环境复杂、画面中同时存在大量样本,而且现有目标检测方法对低质量样本考虑不充分,致使传统目标检测模型检测不准。该文提出一种基于YOLOv11-MW的轻量化农作物实时检测模型,在骨干网络的跨阶段部分连接与空间注意力结构(Cross Stage Partial with Spatial Attention,C2PSA)中提出一种基于混合局部通道注意力(Mixed Local Channel Attention,MLCA)机制模块,增强模型对大量样本的特征提取能力,可以克服复杂背景干扰并提升检测精度;将损失函数替换为Wise-IOU提出一种新的检测头WIOUv3-Detect(WDetect),降低因几何因素对低质量样本的过度惩罚,减少误识别、漏识别。实验结果表明,文中算法在满足边缘算力需求且提升计算速度的前提下,小麦样本检测精度mAP提升1.2%,葡萄样本提升1.9%,更均衡、更准确地进行农作物检测。展开更多
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
文摘植被侵蚀是导致长城结构坍塌的重要潜在因素,相关检测研究主要包括人工巡检与自动化模型。前者现场勘查效率低下;后者受限于小目标漏检与计算冗余,难以实现精准高效的监测。因此,提出一种基于YOLOv11网络架构改进的长城植被侵蚀智能检测算法GWVE-YOLO(the Great Wall vegetation erosion,you only look once)。通过新增P2尺度小目标检测层,增强高分辨率浅层特征提取能力,显著降低微小病害目标的漏检率;在主干与颈部浅层引入RCS-OSA(reparameterized convolution based on channel shuffle of one-shot aggregation)模块,通过通道重排与结构重参数化技术优化特征融合效率,在维持精度的同时降低计算冗余;采用动态非单调聚焦机制的W-IoU(wise intersection over union)v3损失函数,自适应调整低质量样本梯度权重,以提升复杂背景下小目标定位精度。在自建无人机长城病害数据集,经实验表明,所提方法的m AP50达到78.8%、精确率达到79.8%,每秒帧数114,综合性能优于YOLO系列及其已有改进等主流检测模型。
基金supported by the National Natural Science Foundation of China(Grant No.62033007)the Major Fundamental Research Program of Shandong Province(Grant No.ZR2023ZD37).
文摘Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.
文摘Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.
文摘采摘机器人或无人机等智能农业装备在进行视觉检测作业时往往背景环境复杂、画面中同时存在大量样本,而且现有目标检测方法对低质量样本考虑不充分,致使传统目标检测模型检测不准。该文提出一种基于YOLOv11-MW的轻量化农作物实时检测模型,在骨干网络的跨阶段部分连接与空间注意力结构(Cross Stage Partial with Spatial Attention,C2PSA)中提出一种基于混合局部通道注意力(Mixed Local Channel Attention,MLCA)机制模块,增强模型对大量样本的特征提取能力,可以克服复杂背景干扰并提升检测精度;将损失函数替换为Wise-IOU提出一种新的检测头WIOUv3-Detect(WDetect),降低因几何因素对低质量样本的过度惩罚,减少误识别、漏识别。实验结果表明,文中算法在满足边缘算力需求且提升计算速度的前提下,小麦样本检测精度mAP提升1.2%,葡萄样本提升1.9%,更均衡、更准确地进行农作物检测。
