基于改进YOLOv5深度学习的海上船舶识别算法

Recognition algorithm of marine ship based on improved YOLOv5 deep learning

为提高多目标和雾天环境下的海上船舶识别准确率,提出一种基于改进YOLOv5深度学习的海上船舶识别模型(SE-NMS-YOLOv5),该模型结合暗通道去雾算法(Dark channel),并融合了SE(squeeze-and-excitation)注意力机制模块和改进非极大值抑制模型,对船舶数据集进行训练和测试。结果表明:在船舶识别任务上,SE-NMS-YOLOv5模型的准确率、召回率和F1值分别为90.6%、89.9%、90.5%,检测效果比YOLOv5模型分别提升了6.3%、4.8%、5.8%,比YOLOv4模型分别提升了19.1%、19.0%、19.3%;在雾天船舶识别任务上,SE-NMS-YOLOv5-Dark channel模型的准确率、召回率和F1值分别为88.1%、87.2%、87.6%,比SE-NMS-YOLOv5模型的检测结果分别提升了13.8%、13.3%、13.5%。研究表明,SE-NMS-YOLOv5海上船舶识别模型有效地解决了多目标和雾天条件下,海上船舶检测准确率低的问题,提升了船舶检测和识别的整体效果。

In order to improve the accuracy of marine ship recognition in multiple targets and foggy environments,a marine ship recognition model SE-NMS-YOLOv5 is proposed based on improved YOLOv5 deep learning.The model is combined with Dark channel defogging algorithm,SE(squeeze-and-congestion)attention mechanism module and improved non-maximum suppression model for training and testing of ship data sets.The results showed that in the ship recognition task,there was the accuracy of 90.6%,recall rate of 89.9%and SE-NMS-YOLOv5 F1 value of 90.5%,and compared with YOLOv5 model,the detection effect is improved by 6.3%,4.8%and 5.8%.Compared with YOLOv4,the model improved 19.1%,19.0%and 19.3%.In foggy conditions,the accuracy,recall rate and F1 value of SE-NMS-YOLOv5-Dark channel model were 88.1%,87.2%and 87.6%,compared with SE-NMS-YOLOv5 model,the detection results are improved by 13.8%,13.3%and 13.5%,respectively.The findings indicate that the marine ship recognition method based on SE-NMS-YOLOv5 effectively solves the problem of low accuracy of marine ship detection on multiple targets and foggy conditions,and improve the overall effect of ship detection and recognition.