摘要
为解决弹着点水柱目标准确且快速检测的问题,充分平衡检测精度和实时性要求,首先通过将轻量级深度卷积神经网络MobileNetv3与YOLOv4算法结合,并利用3×3的深度可分离卷积代替PANet中的普通卷积,构建了用于水柱检测的网络模型M-YOLOv4;然后,从检测精度、模型容量和运行速度等方面将M-YOLOv4与YOLOv3、YOLOv4和YOLOv4-tiny等进行比较。研究结果表明:M-YOLOv4对水柱目标具有良好的检测效果,能够达到与YOLOv4相当的检测精度,并且参数量显著减少、运行速度更快。
In order to solve the problem of accurate and rapid detection of water column targets at impact points and fully balance detection accuracy and real-time requirements,MobileNet v3,a lightweight deep convolutional neural network,was combined with YOLOv4 algorithm,and the standard convolution in PANet was replaced by 3×3 deep separable convolution.On this basis,M-YOLOv4 was built for water column detection.Next,this model was compared with YOLOv3,YOLOv4 and YOLOv4-tiny network models in the aspect of detection precision,model capacity and running speed.The results show that M-YOLOv4 is of favorable detection effect of water columns,reaching the detection precision equivalent to that of YOLOv4.Besides,the parameter quantity is significantly reduced with higher operating speed.
作者
王智
石章松
吴鹏飞
吴中红
祁江鑫
WANG Zhi;SHI Zhang-song;WU Peng-fei;WU Zhong-hong;QI Jiang-xin(College of Weaponry Engineering,Naval Univ.of Engineering,Wuhan 430033,China)
出处
《海军工程大学学报》
CAS
北大核心
2022年第6期35-40,共6页
Journal of Naval University of Engineering
基金
国家自然科学基金资助项目(61773395)。
