面部表情是传递情感的重要信息,是家畜生理、心理和行为的综合反映,可以用于评估家畜福利。由于家畜面部肌群结构简单,因此家畜面部不同区域的细微变化对于表情的反映较难识别。该研究提出一种基于多注意力机制级联LSTM框架模型(Multi-a...面部表情是传递情感的重要信息,是家畜生理、心理和行为的综合反映,可以用于评估家畜福利。由于家畜面部肌群结构简单,因此家畜面部不同区域的细微变化对于表情的反映较难识别。该研究提出一种基于多注意力机制级联LSTM框架模型(Multi-attention Cascaded Long Short Term Memory,MA-LSTM)对家猪时序面部表情进行分类识别。首先通过简化的多任务级联卷积结构实现帧图像中猪脸的快速检测与定位,去除非猪脸区域对于识别性能的影响。其次提出一种多注意力机制模块,利用不同特征通道视觉信息不同相应峰值响应区域也不同这一特性,通过对峰值响应相近区域进行聚类捕获表情变化引起的面部显著性区域,实现对面部细微变化的关注。在自标注构建的家猪表情数据集上的试验结果表明,该研究提出的多注意力机制级联LSTM模型在4类表情的平均识别准确率为91.826%,对比关闭多注意力机制模块平均识别准确率平均提升6.3个百分点,同时误分率也有较为明显的降低。对比其他常用面部表情识别算法LBP-TOP、HOG-TOP、ELRCN、STC-NLSTM,MA-LSTM模型平均识别精度分别提升约32.6、18.0、5.9和4.4个百分点。试验结果验证了该研究提出的多注意力机制级联LSTM模型在猪脸表情识别的有效性。展开更多
Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditi...Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.展开更多
In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm...In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.展开更多
文摘面部表情是传递情感的重要信息,是家畜生理、心理和行为的综合反映,可以用于评估家畜福利。由于家畜面部肌群结构简单,因此家畜面部不同区域的细微变化对于表情的反映较难识别。该研究提出一种基于多注意力机制级联LSTM框架模型(Multi-attention Cascaded Long Short Term Memory,MA-LSTM)对家猪时序面部表情进行分类识别。首先通过简化的多任务级联卷积结构实现帧图像中猪脸的快速检测与定位,去除非猪脸区域对于识别性能的影响。其次提出一种多注意力机制模块,利用不同特征通道视觉信息不同相应峰值响应区域也不同这一特性,通过对峰值响应相近区域进行聚类捕获表情变化引起的面部显著性区域,实现对面部细微变化的关注。在自标注构建的家猪表情数据集上的试验结果表明,该研究提出的多注意力机制级联LSTM模型在4类表情的平均识别准确率为91.826%,对比关闭多注意力机制模块平均识别准确率平均提升6.3个百分点,同时误分率也有较为明显的降低。对比其他常用面部表情识别算法LBP-TOP、HOG-TOP、ELRCN、STC-NLSTM,MA-LSTM模型平均识别精度分别提升约32.6、18.0、5.9和4.4个百分点。试验结果验证了该研究提出的多注意力机制级联LSTM模型在猪脸表情识别的有效性。
文摘Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.
文摘In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.
