The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3  3 convolution, 1  1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320  320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320  320, ShipYOLO, compared to YOLO-V4.

中文翻译：

---

ShipYOLO：船舶检测的增强模型

船舶检测在辅助智能船舶导航中的应用，对模型的检测速度和精度有着严格的要求。针对这一问题，本研究采用改进的YOLO-V4检测模型（ShipYOLO）对船舶进行检测。与 YOLO-V4 相比，该模型有三个主要改进。首先，优化YOLO-V4的骨干网络（CSPDarknet）。在训练过程中，3   3卷积，1  1卷积，使用恒等并行模式替换原来的特征提取组件（ResUnit），提取更多的特征。在推理过程中，将分支参数组合起来，形成一个名为RCSPDarknet的新骨干网络，在提高模型推理速度的同时提高了准确率。其次，为了解决小尺度船舶漏检问题，我们设计了一种新的放大感受野模块DSPP，具有扩张卷积和Max-Pooling，提高了模型对小尺度船舶空间信息的获取和鲁棒性舰船目标空间位移。最后，我们利用attention机制和Resnet的shortcut思想对YOLO-V4的特征金字塔结构（PAFPN）进行改进，得到一个新的特征金字塔结构，命名为AtFPN。该结构有效提高了模型对不同尺度船舶的特征提取效果，减少了模型参数的数量，进一步提高了模型的推理速度和检测精度。此外，我们创建了一个总共有 2238 张图像的船舶数据集，这是一个单类别数据集。实验结果表明，即使在不同的输入大小下，ShipYOLO 也具有速度更快、准确率更高的优势。考虑到输入大小为 320 实验结果表明，即使在不同的输入大小下，ShipYOLO 也具有速度更快、准确率更高的优势。考虑到输入大小为 320 实验结果表明，即使在不同的输入大小下，ShipYOLO 也具有速度更快、准确率更高的优势。考虑到输入大小为 320  320在搭载NVIDIA 1080Ti GPU的PC上，ShipYOLO的FPS和mAP@5:5:95（mAP90）分别提升了23.7%和13.6%（10.6%），输入大小为  320320，ShipYOLO，与 YOLO-V4 相比。