YOLO-sea:Improved Complex Undersea Target Detection Algorithm for YOLOv7-tiny
The poor quality and low resolution of seabed imaging lead to blurred target edges and difficulty in identifica-tion.The aggregation of small targets increases the risk of missed detection and false detection.To address these prob-lems,the YOLO-sea network detection algorithm is designed based on the YOLOv7-tiny algorithm,which combines high accuracy with small size.Firstly,to address the problems of insufficient feature learning of small targets in low-resolution scenes and easy loss of fine-grained information,the backbone network is redesigned based on SPDConv(space-to-depth convolution)to improve the ability to extract features of dense small targets in low-resolution scenes.Secondly,to address the problems of blurred seabed imaging and difficulty in identifying target edges,a parameter-shared contrast enhanced attention mechanism(PSCEA)is designed to optimize the representation of local details and edge information.Thirdly,based on the GELAN architecture of YOLOv9 and the idea of DSConv(dynamic snake convolution),an efficient aggrega-tion module DSCELAN(DSC-GELAN)is designed to reduce the weight while enhancing the focusing ability on slender targets such as sea cucumbers and fish on the seabed.Finally,the detection head is reconstructed to further improve the detection effect of small targets.The improved model YOLO-sea algorithm has improved mAP by 2.8 percentage points on the DUO dataset and has reduced the number of parameters by 41%,proving the advantages of this innovation in seabed detection.In addition,attention comparison experiments are conducted on the mainstream networks YOLOv5s,YOLOv7-tiny and YOLOv8n.After adding the PSCEA mechanism,the mAP has increased by 1.1,1.3 and 0.7 percentage points respectively,proving the generalization and effectiveness of the mechanism.
万方数据
维普
