To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM...To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.展开更多
针对现有交通监控场景下车辆目标检测算法参数多、计算量大,难以在资源有限的设备中部署的问题,提出一种基于YOLOv8改进的轻量型车辆目标检测算法GSE-YOLO。结合Ghost卷积技术,设计出一种轻量型特征提取模块C2fGhostv2,在减少计算负担...针对现有交通监控场景下车辆目标检测算法参数多、计算量大,难以在资源有限的设备中部署的问题,提出一种基于YOLOv8改进的轻量型车辆目标检测算法GSE-YOLO。结合Ghost卷积技术,设计出一种轻量型特征提取模块C2fGhostv2,在减少计算负担的同时保证良好的特征提取能力。在颈部网络,引入SA(shu ffl e attention)注意力机制,主动选择合适的特征图权重凸显重要特征信息,减少背景对车辆检测的干扰。引入新的损失函数EIOU,解决边界框的纵横比模糊问题,提高预测框精度。实验结果表明,在交通数据集UA-DETRAC上,GSE-YOLO在检测精度没有损失的情况下,相较于原始YOLOv8参数量降低36.11%,计算量降低29.21%,更适合在计算量有限的边缘设备上部署,具有实用价值。展开更多
Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding cost...Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding costs,and promoting sustainable production.This study aimed to develop a novel pig behavior recognition method using advanced computer vision technology to support intelligent livestock farming.Design/methodology/approach-The YOLOv5 model was utilized to achieve contactless and efficient monitoring of daily pig activities.The study enhanced the YOLOv5 model by improving its input mechanism,backbone network and by incorporating the shuffle attention module.These modifications significantly improved the ability of the model to capture and interpret the spatiotemporal features of pig behavior.Findings-The experimental results demonstrate that compared with the original YOLOv5 model,the Precision,Recall,mAP@0.5 and mAp@0.5:0.95 of the proposed model has improved by 3.0%,2.3%,2.6%and 10.5%,respectively.These findings showcase the model’s effectiveness and potential for real-world applications in intelligent livestock farming,and highlight the feasibility of employing advanced computer vision models to enhance monitoring and management in animal farming environments.Originality/value-This study presents a novel approach to pig behavior recognition by integrating cutting-edge computer vision techniques with YOLOv5 enhancements.This study contributes to the field by addressing the challenges of spatiotemporal feature extraction and demonstrating the practical application of these methods in intelligent livestock farming.Future research directions include generalization to other animal species,integration with other sensor data,teal-time monitoring and decision support and application in wildlife and laboratory animal research,thus further advancing the intelligent breeding industry.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.12204062the Natural Science Foundation of Shandong Province under Grant No.ZR2022MF330。
文摘To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.
文摘针对现有交通监控场景下车辆目标检测算法参数多、计算量大,难以在资源有限的设备中部署的问题,提出一种基于YOLOv8改进的轻量型车辆目标检测算法GSE-YOLO。结合Ghost卷积技术,设计出一种轻量型特征提取模块C2fGhostv2,在减少计算负担的同时保证良好的特征提取能力。在颈部网络,引入SA(shu ffl e attention)注意力机制,主动选择合适的特征图权重凸显重要特征信息,减少背景对车辆检测的干扰。引入新的损失函数EIOU,解决边界框的纵横比模糊问题,提高预测框精度。实验结果表明,在交通数据集UA-DETRAC上,GSE-YOLO在检测精度没有损失的情况下,相较于原始YOLOv8参数量降低36.11%,计算量降低29.21%,更适合在计算量有限的边缘设备上部署,具有实用价值。
基金supported by the Natural Science Foundation of Fujian Province(2022J011178)the young and middle-aged teachers education research project of Fujian Province(JAT210422)the Sanming College Scientific Research Development Fund(B202103).
文摘Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding costs,and promoting sustainable production.This study aimed to develop a novel pig behavior recognition method using advanced computer vision technology to support intelligent livestock farming.Design/methodology/approach-The YOLOv5 model was utilized to achieve contactless and efficient monitoring of daily pig activities.The study enhanced the YOLOv5 model by improving its input mechanism,backbone network and by incorporating the shuffle attention module.These modifications significantly improved the ability of the model to capture and interpret the spatiotemporal features of pig behavior.Findings-The experimental results demonstrate that compared with the original YOLOv5 model,the Precision,Recall,mAP@0.5 and mAp@0.5:0.95 of the proposed model has improved by 3.0%,2.3%,2.6%and 10.5%,respectively.These findings showcase the model’s effectiveness and potential for real-world applications in intelligent livestock farming,and highlight the feasibility of employing advanced computer vision models to enhance monitoring and management in animal farming environments.Originality/value-This study presents a novel approach to pig behavior recognition by integrating cutting-edge computer vision techniques with YOLOv5 enhancements.This study contributes to the field by addressing the challenges of spatiotemporal feature extraction and demonstrating the practical application of these methods in intelligent livestock farming.Future research directions include generalization to other animal species,integration with other sensor data,teal-time monitoring and decision support and application in wildlife and laboratory animal research,thus further advancing the intelligent breeding industry.
