【摘 要】
：
针对声纳图像中小目标检测识别率低、虚警率高的问题,提出一种改进的YOLOv3算法.改进的YOLOv3网络在原始YOLOv3的基础上进行优化,改变网络的层级连接,融合更浅层的特征与深层特征,形成新的更大尺度的检测层,提高了网络对水下小目标检测的能力;同时,使用线性缩放的K-means聚类算法优化计算先验框个数和宽高比,提高了先验框与ground truth box之间的匹配度,较原始YOLOv3算法
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针对声纳图像中小目标检测识别率低、虚警率高的问题,提出一种改进的YOLOv3算法.改进的YOLOv3网络在原始YOLOv3的基础上进行优化,改变网络的层级连接,融合更浅层的特征与深层特征,形成新的更大尺度的检测层,提高了网络对水下小目标检测的能力;同时,使用线性缩放的K-means聚类算法优化计算先验框个数和宽高比,提高了先验框与ground truth box之间的匹配度,较原始YOLOv3算法均值平均精度提高了 7%.实验结果表明,所提出的改进YOLOv3算法能够有效分类与识别小目标且有更高的准
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