海洋测绘2024，Vol.44Issue(4)：21-25,5.DOI:10.3969/j.issn.1671-3044.2024.04.005
基于轻量化YOLOv7算法的侧扫声纳图像沉船检测
Side-scan sonar image shipwreck detection based on lightweight YOLOv7 algorithm
摘要
Abstract
For the existing side-scan sonar underwater shipwreck detection method,there are deficiencies in the detection speed and leakage detection in YOLOv5.This paper proposes an improved method for underwater wreck detection based on the lightweight YOLOv7 algorithm.First,the sampling numbers of shipwreck images are expanded by random flip,random noise and other operations.Second,a transfer learning strategy is introduced to transfer the weights learned on the COCO dataset to the YOLOv7 network for shipwreck detection.Third,the computation of the penalty term in the loss function of the model is improved to enhance the speed of convergence.Finally,a FasterNet structure is introduced into the YOLOv7 network,which reduces the number of parameters and the computational complexity of the model,and reduces the hardware requirement of the model to achieve lightweight model.The experimental results show that the improved method improves the class mean accuracy value(mAP value)by 4.75%compared with the original YOLOv7 algorithm,and the detection speed is also improved from 0.021 8 fps to 0.017 9 fps,which proves the value of the improved method in this paper for engineering applications.关键词
侧扫声纳图像/沉船检测/YOLOv7算法/FasterNet结构/迁移学习Key words
side-scan sonar images/shipwreck detection/YOLOv7/FasterNet/transfer leaering分类
天文与地球科学引用本文复制引用
王胜平,刘娉婷,陈晓红,陈志高..基于轻量化YOLOv7算法的侧扫声纳图像沉船检测[J].海洋测绘,2024,44(4):21-25,5.基金项目
国家自然科学基金(42266006) （42266006）
自然资源部海洋环境探测技术与应用重点实验室开放基金(MESTA-2020-A002) （MESTA-2020-A002）
江西省重点研发计划(20212BBE53031). （20212BBE53031）
