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Received：24 December 2021，
Revised：2022-05-18，
Published：16 April 2023
移动端阅览
目的
2
舰船舷号检测识别是海面态势感知的关键技术，精准的舷号检测识别对海洋权益保护具有重要意义。但目前没有公开数据提供支持。为此，本文先构建了一个真实场景下的稀疏舰船舷号数据集（sparse ship hull number dataset in real scene，SSHN-RS），包含3 004幅舰船图像，共计11 328个舷号字符，覆盖了多国、各类、水平、倾斜、背景简单、背景复杂、光线不佳和被遮挡的舰船舷号样本，是一个具有挑战性的数据集。基于SSHN-RS，开展舰船舷号检测识别研究，其主要难点在于：1）样本稀疏，模型容易过拟合；2）舷号字符分布密集，网络难以充分提取各字符特征；3）部分字符存在嵌套区域和相似区域，网络会识别出大量冗余结果。针对上述难点，提出了一种基于多视角渐进式上下文解耦的舰船舷号检测识别算法。
方法
2
首先，引入一个固定中心和最大化面积的随机透视变换技术，在不增加样本数量的前提下扩充舷号姿态，实现了数据增广，提升了模型的泛化能力；其次，提出了一个渐进式上下文解耦技术，先通过依次擦除舷号各字符生成一系列新样本，再利用特征提取网络提取和融合各样本的多尺度特征，不仅减少字符上下文信息对特征学习的干扰，而且再次增广了数据；最后，在测试阶段，提出了一个掩码间扰动抑制技术，先根据预测结果采用与渐进式上下文解耦技术类似的方法生成新样本并重新进行预测，再引入一个1维非极大值抑制技术去除预测结果中错误的冗余字符，输出最佳检测识别结果，进一步优化网络性能。
结果
2
在SSHN-RS上采用主流实例分割算法进行定性和定量评估。在定量评估上，本文算法舷号的检测精确率、召回率、
F
值和识别率分别可达0.985 4，0.957 6，0.971 3，0.901 8，均优于其他算法。相比指标排名第2的算法，分别提高了4.51%，3.45%，3.97%，8.83%；在定性评估上，本文算法更适合舰船舷号检测识别任务，检测识别性能更高。此外，本文算法可以泛化到其他实例分割算法中，以经典算法Mask RCNN（mask region based convolutional neural network）为例，加入本文算法各模块后，各指标分别提升了9.82%，6.04%，7.80%，6.73%。
结论
2
本文算法可以解决舷号检测识别任务中因样本稀疏、舷号分布密集、部分字符存在嵌套和相似性带来的问题，在主观和客观上均取得了最先进的性能，并且具有通用性。SSHN-RS可通过
https://github.com/Bingchuan897/SSHN-RS
https://github.com/Bingchuan897/SSHN-RS
获取。
Objective
2
Ship hull number detection and recognition can be as the key technologies for marine awareness. It is essential for the preservation of maritime rights and interests. However， it is required to data-driven researches in support of ship hull number detection and recognition. Therefore， we develop a sparse ship hull number dataset in real scene （SSHN-RS）， which contains 3 004 images with a total of 11 328 hull numbers. The challenging SSHN-RS dataset is featured of ship hull numbers of various countries， hull numbers of various types， horizontal hull numbers， inclined hull numbers， hull numbers with complex background， hull numbers with simple background， poorly illuminated hull numbers and partially occluded hull numbers. We carry out SSHN-RS-related research on ship hull number detection and recognition. The main challenges are required to be resolved on three aspects as following： 1） the hull number samples are sparse， which causes over-fitting of the network， 2） the features of hull number are densely distributed， which is challenged to learn some of the hull number characteristics fully， and 3） some hull number have its nested areas and a high degree of similarity， which is costly for large number of redundant results.
Method
2
To resolve these problems mentioned above， we demonstrate a ship hull number detection and recognition algorithm in terms of multi-view progressive context decoupling. First， a random perspective transformation technology with fixed center and maximized area is illustrated. To realize data augmentation and improve the generalization ability of the model， the hull number spatial attitude is extended without increasing the number of samples. Second， a progressive context decoupling technology is proposed. A series of new samples are first generated by sequentially erasing each character of the hull number， and the feature extraction network is then used to extract and fuse the multi-scale features of each sample. It can reduce the influence of feature-contextual information on feature learning and rich the data expansion. It can improve the feature expression ability effectively as well. Finally， in the testing stage， to generate new samples， and inputs the new samples into the testing network for prediction， an inter mask disturbance suppression technology is first focused on a method similar to the progressive context decoupling technology. At the same time， a one-dimensional non-maximum suppression technology is introduced that it can mainly process the hull number recognition results of each sample twice. To suppress the redundant mask in the detection and recognition results effectively， it can make each character of the hull number of all samples correspond to a recognition result. For the testing network， the output has only a set of optimal results. However， there is hull number noisy recognition in some samples. Therefore， the hull number recognition results of all samples are added on the original images， and the second one-dimensional non-maximum suppression technology is performed and processed on it. It can suppress the noises in some samples and outputs a set of optimal results. The post-processing module can optimize the detection and recognition performance further.
Result
2
The comparative experiment is mainly carried out on SSHN-RS. First， we conduct evaluation analysis on the general instance segmentation algorithms. Multi-view progressive context decoupling-based detection precision， recall， f-score and recognition rate of the ship number detection and recognition algorithm can reach 0.985 4， 0.957 6， 0.971 3 and 0.901 8， which are improved by 4.51%， 3.45%， 3.97% and 8.83% respectively compared to the second-ranked method. Second， the ablation experiments on the ship hull number detection and recognition algorithm are carried out in terms of multi-view progressive context decoupling. The experimental results show that the performance of ship hull number detection and recognition is improved whether each technology is used solely or mixed. Finally， to validate its effectiveness， we apply some popular modules of the ship hull number detection and recognition algorithm based on multi-view progressive context decoupling to other general instance segmentation algorithms. Taking the classic algorithm mask region based convolutional neural network（Mask RCNN） as an example， the indexes have been improved by 9.82%， 6.04%， 7.80% and 6.73% of each after our algorithm module is added.
Conclusion
2
Our SSHN-RS contains rich and effective ship hull number information， which can provide data support for ship hull number detection and recognition. The experimental results show that the multi-view progressive context decoupling-based ship hull number detection and recognition algorithm can optimize the detection and recognition performance of hull numbers due to sparse samples， dense character distribution， nested areas， and characters-between high similarity. This algorithm can be generalized to other deep learning based segmentation algorithms. The benchmarks-relevant are provided as a basis for future research on ship hull number detection and recognition. The dataset is available at
https://github.com/Bingchuan897/SSHN-RS
https://github.com/Bingchuan897/SSHN-RS
.
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