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
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.
