基于Darknet网络和YOLOv3算法的船舶跟踪识别  

Ship tracking and recognition based on Darknet network and YOLOv3 algorithm

作　　者：刘博 王胜正[1] 赵建森[1] 李明峰 LIU Bo;WANG Shengzheng;ZHAO Jiansen;LI Mingfeng(Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学商船学院

出　　处：《计算机应用》2019年第6期1663-1668,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51379121,61304230);上海市曙光人才计划项目(15SG44)~~

摘　　要：针对我国沿海与内陆水域区域视频监控处理存在实际利用率低、误差率大、无识别能力、需人工参与等问题,提出基于Darknet网络模型结合YOLOv3算法的船舶跟踪识别方法实现船舶的跟踪并实时检测识别船舶类型,解决了重要监测水域船舶跟踪与识别问题。该方法提出的Darknet网络引入了残差网络的思想,采用跨层跳跃连接方式以增加网络深度,构建船舶深度特征矩阵提取高级船舶特征进行组合学习,得到船舶特征图。在此基础上,引入YOLOv3算法实现基于图像的全局信息进行目标预测,将目标区域预测和目标类别预测整合于单个神经网络模型中。加入惩罚机制来提高帧序列间的船舶特征差异。通过逻辑回归层作二分类预测,实现在准确率较高的情况下快速进行目标跟踪与识别。实验结果表明,提出的算法在30 frame/s的情况下,平均识别精度达到89.5%,与传统以及深度学习算法相比,不仅具有更好的实时性、准确性,对各种环境变化具有较好的鲁棒性,而且可以识别多种船舶的类型及其重要部位。Aiming at the problems of low utilization rate, high error rate, no recognition ability and manual participation in video surveillance processing in coastal and inland waters of China, a new ship tracking and recognition method based on Darknet network model and YOLOv3 algorithm was proposed to realize ship tracking and real-time detection and recognition of ship types, solving the problem of ship tracking and recognition in important monitored waters. In the Darknet network of the proposed method, the idea of residual network was introduced, the cross-layer jump connection was used to increase the depth of the network, and the ship depth feature matrix was constructed to extract advanced ship features for combination learning and obtaining the ship feature map. On the above basis, YOLOv3 algorithm was introduced to realize target prediction based on image global information, and target region prediction and target class prediction were integrated into a single neural network model. Punishment mechanism was added to improve the ship feature difference between frames. By using logistic regression layer for binary classification prediction, target tracking and recognition was able to be realized quickly with high accuracy. The experimental results show that, the proposed algorithm achieves an average recognition accuracy of 89.5% with the speed of 30 frame/s;compared with traditional and deep learning algorithms, it not only has better real-time performance and accuracy, but also has better robustness to various environmental changes, and can recognize the types and important parts of various ships.

关 键 词：海上交通 船舶监测 船舶跟踪 船舶类型识别 Darknet网络 YOLOv3算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]