基于改进YOLOV3-Tiny的海面船舰目标快速检测  

Fast Target Detection of Surface Ship Based on Improved YOLOV3-Tiny

作　　者：李庆忠[1] 徐相玉 LI Qingzhong;XU Xiangyu(College of Engineering,Ocean University of China,Qingdao,Shandong 266100,China)

机构地区：[1]中国海洋大学工程学院,山东青岛266100

出　　处：《计算机工程》2021年第10期283-289,297,共8页Computer Engineering

基　　金：国家重点研发计划(2017YFC1405202);海洋公益性行业科研专项(201605002)。

摘　　要：为实现海面船舰目标的快速、准确检测,提出一种改进的船舰目标检测算法。在网络结构方面根据船舰目标的特点,对浅层信息进行强化重构以降低小目标的漏检率,同时引入改进的残差网络增加网络深度和降低网络参数计算量,并且采用金字塔网络进行多尺度特征融合,以兼顾图像中大小船舰目标的检测性能。在网络训练中利用迁移学习策略进行网络模型的训练,以克服船舰图像样本集有限的问题。在视频检测中利用帧间图像结构相似度进行选择性网络前向计算,以提高视频帧检测速率。实验结果表明,该算法海面船舰目标检测的准确率达到92.4%,较YOLOV3-Tiny提高7个百分点,召回率达到88.6%,且在CPU平台上船舰目标的检测速度达到12 frame/s。In order to achieve fast and accurate detection of surface ship targets,this paper proposes a ship target detection algorithm based on improved YOLOv3-Tiny.Firstly,in network structure,the features of shallow layers of the network is enhanced and reconstructed according to the characteristics of ship targets to reduce the miss detection rate of small targets,and the improved residual network is introduced to improve the depth of the network while reducing the calculation of network parameters.Moreover,the pyramid network is used for multi-scale feature fusion to balance the detection capability between large ship targets and small ship targets in images.Secondly,in the network training,transfer learning strategy is employed to train the designed network model to alleviate the limitation of known ship image samples.Finally,in video detection,a video frame selection method for forward computation of the network model based on structure similarity of inter frames is proposed to improve the detection frame rate.The experimental results show that the proposed algorithm has precision rate up to 92.4%,with an increase of 7%compared with YOLOV3-Tiny,recall rate up to 84%,and detection frame rate up to 12 frames/s on CPU platform.

关 键 词：卷积神经网络 YOLO网络 船舰目标检测 迁移学习 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]