Rotating target detection algorithm in ship remote sensing images based on YOLOv8
Aiming at the problems of difficulty in detecting small targets in ship remote sensing target image detection,varied ship shapes,and excessive redundant information in traditional horizontal bounding boxes for targets with high aspect ratios,a rotating target detection algorithm for ship remote sensing images based on an improved YOLOv8 was proposed.By improving the convolution structure in the backbone network,the problem of fine-grained information loss caused by stride convolution was alleviated,improving the accuracy of small target detection.By replacing some of the convolution modules in C2f with DCNv3 deformable convolution,the feature information extraction of irregular objects was enhanced,improving the nonlinear modeling capabilities of the model.Integrating the shallow feature map from the backbone network into the neck alleviated the problem of detailed information loss caused by multiple convolution operations,enhancing the detection capability for small target objects.Experimental results showed that the detection accuracy(mAP50)of the improved algorithm on the ShipRSImageNet dataset reached 84.317%,which is 4.054%higher than the baseline model.The model accuracy reached 93.235%on the HRSC2016 dataset,which is 1.555%higher than the baseline model.The improved algorithm achieved high detection performance with a small increase in the number of model parameters,effectively balancing model efficiency and performance.
NETL
NSTL
万方数据
