基于DP-DBNet和MHA-CRNN的船牌号检测与识别
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国家自然科学基金(61806107); 农业部水产养殖数字建设试点项目(2017-A2131-130209-K0104-004); 青岛市创新创业领军人才(15-07-03-0030)


Detection and Recognition of Ship Numbers Based on DP-DBNet and MHA-CRNN
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    摘要:

    船牌号的检测和识别对于港口的智能化管理和解决传统人工方式监管渔船中存在的耗时耗力的问题具有重要意义. 针对船牌悬挂位置, 背景颜色和字符个数不统一等特点, 本文提出两阶段双模型的检测和识别方法. 首先, 提出将双路径网络(dual path networks, DPN)与可微二值化网络(differentiable binarization network, DBNet)相结合的DP-DBNet船牌号位置检测模型. 其次, 提出将多头注意力机制(multi-head-attention mechanism, MHA)与改进的卷积循环神经网络(convolutional recurrent neural network, CRNN)相结合的MHA-CRNN船牌号文字识别模型. 最后, 以烟台芝罘区新型现代化智慧渔港项目为数据来源, 并进行算法对比实验分析; 实验结果表明, 两种模型结合的两阶段识别方法可以使船牌号的识别准确率达到76.39%, 充分证明了该模型的有效性和在海洋港口管理方面的应用价值.

    Abstract:

    The detection and recognition of ship numbers are of great significance for the intelligent management of ports and can solve the time-consuming and labor-intensive problems caused by the traditional manual supervision of fishing boats. Since the ship number plates feature non-uniform hanging positions, background colors, and numbers of characters, this study proposes a two-stage detection and recognition method with two models. First, the study introduces a DP-DBNet ship number location detection model that combines dual path networks (DPN) with a differentiable binarization network (DBNet). Secondly, the study presents an MHA-CRNN ship number recognition model that combines the multi-head-attention mechanism (MHA) with the improved convolutional recurrent neural network (CRNN). Finally, this study uses the data from the new modern smart fishing port project in Zhifu District of Yantai and carries out an algorithm comparison experiment analysis. The experimental results show that the two-stage recognition method with two models can make the recognition accuracy rate of the ship number reach 76.39%, which fully proves the effectiveness and application value of the model in marine port management.

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丁东平,李海涛.基于DP-DBNet和MHA-CRNN的船牌号检测与识别.计算机系统应用,2023,32(3):209-216

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  • 收稿日期:2022-07-26
  • 最后修改日期:2022-08-26
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  • 在线发布日期: 2022-11-29
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