基于加强特征融合的轻量化船舶目标检测

Lightweight Ship Target Detection Based on Enhanced Feature Fusion

针对基于深度学习的海上船舶目标检测任务中存在检测网络复杂且参数量大、检测实时性差的问题,提出一种加强特征融合的轻量化YOLOv4算法——MA-YOLOv4.首先使用MobileNetv3替换主干网络,引入新的激活函数SiLU并使用深度可分离卷积代替普通3×3卷积降低网络参数量;其次加入自适应空间特征融合模块加强特征融合;最后使用MDK-means聚类算法得到适用于船舶目标的锚框,用Ship7000数据集进行训练和评估.实验结果表明,改进算法与YOLOv4相比,模型参数量降低82%,mAP提高2.57%,FPS提高30帧/s,能实现对海上船舶的高精度实时检测.

A lightweight YOLOv4 algorithm,MA-YOLOv4,is proposed to enhance feature fusion for addressing problems of complex detection networks,a large number of parameters,and poor real-time detection in deep learningbased maritime ship target detection tasks.Firstly,MobileNetv3 is employed to replace the backbone network,and a new activation function SiLU is introduced.The depthwise separable convolution is applied to replace the ordinary 3×3 convolution to reduce the number of network parameters.Secondly,an adaptive spatial feature fusion module is added to enhance feature fusion.Finally,the MDK-means clustering algorithm is adopted to get the anchor frame suitable for the ship target,and the Ship7000 dataset is utilized for training and evaluation.Experimental results show that compared with YOLOv4,the improved algorithm can reduce the number of model parameters by 82%and increase mAP by 2.57%and FPS by 30 f/s,which can achieve high-precision real-time detection of ships at sea.