一种基于改进YOLOv4的SAR舰船检测算法

A SAR ship detection algorithm based on improved YOLOv4

在大量数据支持的背景下,如何高效利用大量SAR图像,提升舰船目标的检测精度是当前舰船目标检测的难题。本文聚焦如何提升YOLOv4算法对SAR舰船目标的检测精度,提出了一种融合多尺度和注意力增强的YOLOv4增强算法。在原YOLOv4的PANet中加入注意力模块(CBAM),同时使用加强的K-means聚类算法对数据集中的舰船目标真实框进行聚类,并对锚框结果进行线性比例变换,让算法锚框更适合于训练集。实验证明本文提出的算法在SAR舰船检测中的平均准确率(mAP)达到了94.05%,比原始YOLOv4精度提高了0.7%。实验结果充分证明本文提出的算法能够提升SAR舰船图像检测精度,为海上活动判断精确化提供技术支持。

Under the background of large amount of data support, how to use a large number of SAR images efficiently and improve the accuracy of ship target detection is the current problem of ship target detection. This paper focuses on how to improve the accuracy of YOLOv4 algorithm for SAR ship target detection, and presents a YOLOv4 enhancement algorithm that combines multiscale and attention enhancement. The Attention Module(CBAM) is added to the PANet of the original YOLOv4, and the enhanced K-means clustering algorithm is used to cluster the ship target real frame in the dataset, and the result of the anchor frame is transformed linearly to make the algorithm anchor frame more suitable for the training set. Experiments show that the average accuracy(mAP) of the proposed algorithm in SAR ship detection is 94.05%, which is 0.7% higher than that of the original YOLOv4. The experimental results fully demonstrate that the proposed algorithm can improve the accuracy of SAR ship image detection and provide technical support for the accuracy of sea activities judgment.