基于MSAM-YOLOv5的内河航道船舶识别方法

XIAO Zheng,WANG Jiye, XIA Yeliang

Journal of Huazhong University of Science and Technology(Nature Science Edition)(2023)

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Abstract
针对内河航道上无人船识别目标时受背景复杂性和分布多样性影响而存在漏检的问题,提出一种基于YOLOv5(you only look once)的算法.首先,提出一种注意力模块MSAM(多尺度注意力模块),可对带有大量空间信息的浅层特征图和带有丰富语义信息的深层特征图进行注意力融合,使得融合后的特征图具有更强的特征;然后,研究MSAM模块的不同位置的影响;最后,优化锚框参数,使得锚框形状更加符合内河船舶的形状.在船舶数据集上进行实验,结果表明:本算法的召回率提高了1.12%,三个mAP(平均精度均值)指标分别提高了0.87%,5.00%和2.07%,FPS(帧率)指标提高了3,漏检率降低,整体检测准确性和检测速度均得到提升.
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Key words
ship detection,inland waterways,multiscales attention module,YOLOv5,attention module position
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