Research on Ship Small Target Detection Method Based on YOLOv5s
SHI Hongyu
CAI Zigui
DU Wen
ZHANG Zheyu
Abstract：The detection of ship target on sea surface is easy to be interfered by the background such as land and sea wave.Aiming at the problems of low precision and poor robustness of ship small target detection,an improved ship target detection model CWMA-YOLOv5s is proposed.Firstly,a C2f module with multi-branch cross-layer connections is designed to enrich multi-target ship gradient flow information.Then,a residual polytope self-attention fusion module is designed and implemented to optimize the feature fusion effect.The Predection network is then improved and the SCP structure is designed to increase the saliency of the ship's target.Finally,an improved WIOU loss function is introduced to solve the problems of gradient explosion and early model degrada-tion caused by the CIOU loss function.The experimental results show that the model improves precision by 13.1%,improves recall by 12.8%and improves mAP@50 by 6.8%on the MASATI-v2 dataset compared to the YOLOv5s model.Compared with other detec-tion algorithms of the same type,the algorithm has better learning ability,the overall detection accuracy reaches 82.3%,and has strong robustness.
Keywords：ship detectionmulti-head self-attentionspatial context pyramidWIOU loss function
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 34-38,73 )