Given the challenges of underwater garbage detection,including insufficient lighting,low visibility,high noise levels,and high misclassification rates,this paper proposes a model named CSC-YOLO.CSC-YOLO for detecting ...Given the challenges of underwater garbage detection,including insufficient lighting,low visibility,high noise levels,and high misclassification rates,this paper proposes a model named CSC-YOLO.CSC-YOLO for detecting garbage in complex un-derwater environments characterized by murky water and strong hydrodynamic conditions.The model incorporates the Content-Guid-ed Attention(CGA)attention mechanism into the SPPF module of the YOLOv8 backbone network to enhance dehazing,reduce noise interference,and fuse multi-scale feature information.Additionally,a Single-Head Self-Attention(SHSA)mechanism is introduced in the final layer of the backbone network to achieve local and global feature fusion in a lightweight manner,improving the accuracy of garbage detection.In the detection head,the CBAM attention mechanism is added to further enhance feature representation,increase the model’s target localization,and improve robustness against complex backgrounds and noise.Furthermore,the anchor box coordi-nates from CSC-YOLO are fed into Mobile_SAM to achieve precise segmentation of underwater garbage.Experimental results show that CSC-YOLO achieves a Precision of 0.962,Recall of 0.898,F1-score of 0.929,and mAP0.5 of 0.960 on the ICRA19 trash dataset,representing improvements of 2.9%,1.7%,2.3%,and 2.0%over YOLOv8n,respectively.The combination of CSC-YOLO and Mo-bile_SAM not only enables garbage detection in complex underwater environments but also achieves segmentation.This approach generates additional garbage segmentation masks without manual annotations,facilitating rapid expansion of labeled underwater garbage datasets for training.As an emerging model for intelligent underwater garbage detection,the proposed method holds signifi-cant potential for practical applications and academic research,offering an effective solution to the challenges of intelligent garbage detection in complex underwater environments.展开更多
针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建...针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建空间多尺度卷积注意力(spatial multi-scale convolutional attention,SMCA),提升模型对空间和通道信息的特征提取能力;其次,针对深层网络传递时小目标语义信息容易丢失的问题,设计聚合亚像素卷积(concentrated sub-pixel convolution,CSC),采用多尺度聚合特征提取方法,增强了网络对语义信息的提取能力;最后,将SIoU损失函数替代原模型中的CIoU(complete intersection over union loss)损失函数,加快了网络的收敛速度。SCS-YOLO模型在RSOD和NWPU VHR-10数据集上,平均精确率的平均值(mAP)分别达到97%和90.9%,相较于原模型分别提升了2.2%和2.7%,可见该方法在遥感图像小目标检测任务中的有效性。展开更多
基金support of this research from the National Natural Science Foundation of China(No.12174085)the Key Research and Devel-opment Project of Changzhou,Jiangsu Province(No.CE 20235054).
文摘Given the challenges of underwater garbage detection,including insufficient lighting,low visibility,high noise levels,and high misclassification rates,this paper proposes a model named CSC-YOLO.CSC-YOLO for detecting garbage in complex un-derwater environments characterized by murky water and strong hydrodynamic conditions.The model incorporates the Content-Guid-ed Attention(CGA)attention mechanism into the SPPF module of the YOLOv8 backbone network to enhance dehazing,reduce noise interference,and fuse multi-scale feature information.Additionally,a Single-Head Self-Attention(SHSA)mechanism is introduced in the final layer of the backbone network to achieve local and global feature fusion in a lightweight manner,improving the accuracy of garbage detection.In the detection head,the CBAM attention mechanism is added to further enhance feature representation,increase the model’s target localization,and improve robustness against complex backgrounds and noise.Furthermore,the anchor box coordi-nates from CSC-YOLO are fed into Mobile_SAM to achieve precise segmentation of underwater garbage.Experimental results show that CSC-YOLO achieves a Precision of 0.962,Recall of 0.898,F1-score of 0.929,and mAP0.5 of 0.960 on the ICRA19 trash dataset,representing improvements of 2.9%,1.7%,2.3%,and 2.0%over YOLOv8n,respectively.The combination of CSC-YOLO and Mo-bile_SAM not only enables garbage detection in complex underwater environments but also achieves segmentation.This approach generates additional garbage segmentation masks without manual annotations,facilitating rapid expansion of labeled underwater garbage datasets for training.As an emerging model for intelligent underwater garbage detection,the proposed method holds signifi-cant potential for practical applications and academic research,offering an effective solution to the challenges of intelligent garbage detection in complex underwater environments.
文摘针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建空间多尺度卷积注意力(spatial multi-scale convolutional attention,SMCA),提升模型对空间和通道信息的特征提取能力;其次,针对深层网络传递时小目标语义信息容易丢失的问题,设计聚合亚像素卷积(concentrated sub-pixel convolution,CSC),采用多尺度聚合特征提取方法,增强了网络对语义信息的提取能力;最后,将SIoU损失函数替代原模型中的CIoU(complete intersection over union loss)损失函数,加快了网络的收敛速度。SCS-YOLO模型在RSOD和NWPU VHR-10数据集上,平均精确率的平均值(mAP)分别达到97%和90.9%,相较于原模型分别提升了2.2%和2.7%,可见该方法在遥感图像小目标检测任务中的有效性。
