基于注意力机制改进的无锚框舰船检测模型  

An improved anchor-free model based on attention mechanism for ship detection

作　　者：高云龙 任明 吴川[1] 高文[1] GAO Yun-long;REN Ming;WU Chuan;GAO Wen(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China)

机构地区：[1]中国科学院长春光学精密机械与物理研究所,长春130033

出　　处：《吉林大学学报（工学版）》2024年第5期1407-1416,共10页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61401425);吉林省科技发展计划重点研发项目(2022021146GX)。

摘　　要：为提升模型对合成孔径雷达(SAR)图像多尺度舰船目标的检测能力,保证检测网络的实时性,提出一个基于注意力机制改进的无锚框舰船检测模型。在YOLOX网络特征金字塔处嵌入空洞注意力模块,调节感受野与多尺度融合的关系,强化特征的表示能力。在检测头部设计中心性预测分支,对锚点的分类得分进行加权处理,调整模型的损失函数,优化检测结果。在数据集SSDD上进行的对比实验结果表明:本文提出的模型优于主流的深度网络检测模型,精度达到94.73%,且在检测精度和检测速度中取得最佳平衡。In order to improve the detection capability of detectors for multiscale ships in SAR images and ensure the real-time performance of the detection networks, an improved anchor-free model based on attention mechanism for ship detection is proposed. On the basic framework of the off-the-shelf YOLOX, a lightweight dilated convolutional attention module(DCAM) is embedded in front of feature pyramid network(FPN) to adjust the relationship between receptive field and multiscale fusion, and strengthen the representation ability of features. The detection head is redesigned by introducing the center-ness prediction branch, which can weight the classification scores of the anchor points, in the meantime, the loss function of the proposed model is also revised to optimize the final detection performance. Through the comparative experiments on dataset SSDD, the proposed model in this paper is superior to the mainstream deep learning detection models, with an accuracy of 94.73%, and achieves the best trade-off between detection accuracy and detection speed.

关 键 词：计算机视觉 舰船目标检测 空洞卷积 注意力机制 无锚框 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]