Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基...精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基础框架,在每层解码器的第一个卷积单元后嵌入空洞空间金字塔池化以得到新的解码特征;同时通过密集连接和跳跃连接将不同解码路径的特征融合,采用Lovasz Hinge Loss损失函数来获取全局最优并采用数据增强的方式对原有数据进行扩充。实验结果表明,该算法在Flood Area Segmentation数据集上的IOU值达到80.53%,与目前流行的图像分割算法DeepLabv3、UNet、FCN(Res18)、PspNet、FCN(Res50)、UNet++相比,依次高出7.41、6.21、3.81、3.70、1.92、1.82个百分点。该算法具有较高的分割精度和良好的稳定性,整体性能优异,为实际防洪监测提供了技术支撑。展开更多
With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,...With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.展开更多
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
文摘精准识别与划定可能受到洪水影响的地区对于规划与实施防洪措施十分重要。针对洪水区域识别过程中易受到地形、气候、光照以及数据不平衡等因素影响的问题,提出一种基于改进UNet++的洪水区域图像分割算法。该算法以UNet++分割网络为基础框架,在每层解码器的第一个卷积单元后嵌入空洞空间金字塔池化以得到新的解码特征;同时通过密集连接和跳跃连接将不同解码路径的特征融合,采用Lovasz Hinge Loss损失函数来获取全局最优并采用数据增强的方式对原有数据进行扩充。实验结果表明,该算法在Flood Area Segmentation数据集上的IOU值达到80.53%,与目前流行的图像分割算法DeepLabv3、UNet、FCN(Res18)、PspNet、FCN(Res50)、UNet++相比,依次高出7.41、6.21、3.81、3.70、1.92、1.82个百分点。该算法具有较高的分割精度和良好的稳定性,整体性能优异,为实际防洪监测提供了技术支撑。
文摘With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.
