融合Transformer注意力的舰船要害部位检测

doi:10.3778/j.issn.1002-8331.2402-0099

摘要/Abstract

摘要： 针对舰船及要害部位检测任务的需求，设计了SEC-YOLOv5（Swin Transformer C3EE CBAM-YOLOv5）舰船及要害部位检测算法。SEC-YOLOv5算法利用Swin Transformer改进网络主干部分C3模块，增强模型对语义特征的提取能力，提升要害部位小目标的识别精度；使用CBAM注意力机制加权有效特征信息，提升特征信息利用率；设计C3EE（C3 easy effect）模块替换瓶颈层所有C3模块，扩大模型感受野，丰富模型的梯度信息流。基于RS-ship数据集验证所提算法，以原YOLOv5算法为基准进行逐模块的消融对比实验，实验结果表明，SEC-YOLOv5算法在增加少量参数量的前提下，将平均精度值mAP@0.5提高5个百分点。

关键词: Swin Transfomer, 舰船检测, 卷积神经网络, 深度学习

Abstract: According to the requirements of ships and key parts, this paper designs the detection algorithm of SEC-YOLOv5 ships and key parts. The SEC-YOLOv5 algorithm uses Swin Transformer to improve the C3 module of the backbone network, enhancing the ability to extract semantic features and improving the identification accuracy of small targets in key parts. It also uses the CBAM attention mechanism to weight effective feature information and improve the utilization rate of feature information. The paper designs the C3EE module to replace all C3 modules in the bottleneck layer, expand the feeling field of the model, and enrich the gradient information flow of the model. Based on the RS-ship data set, the proposed algorithm is verified, and the original YOLOv5 algorithm is used as the benchmark for the module ablation comparison experiment. The experimental results show that the SEC-YOLOv5 algorithm increases the average accuracy value mAP@0.5 by 5 percentage points on the premise of increasing a small number of parameters.

Key words: Swin Transformer, ship detection, convolutional neural network, deep learning

高兵, 祝宇鸿. 融合Transformer注意力的舰船要害部位检测[J]. 计算机工程与应用, 2025, 61(11): 284-294.

GAO Bing, ZHU Yuhong. Ship Critical Parts Detection with Transformer Attention[J]. Computer Engineering and Applications, 2025, 61(11): 284-294.

参考文献

[1] PAUL V, MICHAEL J. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001.
[2] DALAL N. Histograms of oriented gradients for human detection[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893.
[3] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.
[4] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the Conference and Workshop on Neural Information Processing Systems, 2012: 1097-1105.
[5] REN S Q, HE K M, ROSS G, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[6] HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
[7] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the European Conference on Computer Vision, 2016: 21-37.
[8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[9] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
[10] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[11] BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004. 10934, 2020.
[12] JOCHER G, SOMMER Y, ALESSANDRINI M, et al. YOLOv5: a state-of-the-art real-time object detection system[J]. arXiv:2006.09882, 2020.
[13] 李海军, 孔繁程, 林云. 基于改进YOLOv5s的红外舰船检测算法[J]. 系统工程与电子技术, 2023, 45(8): 2415-2422.
LI H J, KONG F C, LIN Y. Infrared ship detection algorithm based on improved YOLOv5s[J]. Systems Engineering and Electronics, 2023, 45 (8): 2415-2422.
[14] 刘忻伟, 朴永杰, 郑亮亮, 等. 面向航天光学遥感复杂场景图像的舰船检测[J]. 光学精密工程, 2023, 31(6): 892-904.
LIU X W, PU Y J, ZHENG L L, et al. Ship detection for complex scene images of space optical remote sensing[J]. Optical and Precision Engineering, 2023, 31(6): 892-904.
[15] 林鑫伟, 徐志京, 黄海. 复杂背景下的SAR图像多尺度舰船检测[J]. 中国航海, 2023, 46(2): 17-24.
LIN X W, XU Z J, HUANG H. Multi-scale ship detection in SAR image with complex background[J]. Navigation of China, 2023, 46 (2): 17-24.
[16] 白玉, 迟文恺, 谢宝蓉, 等. 结合目标提取和深度学习的红外舰船检测[J]. 电讯技术, 2023, 63(2): 193-198.
BAI Y, CHI W K, XIE B R, et al. Infrared ship detection combined with target extraction and deep learning[J]. Telecommunication Technology, 2023, 63(2): 193-198.
[17] LIU Z, LIN Y T, CAO Y, et al. SwinTransformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the International Conference on computer Vision, 2021: 10012-10022.
[18] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.
[19] WOO, S. PARK, J, LEE, J Y, et al. CBAM: convolutional block attention module[J]. arXiv:1807,06521, 2018.
[20] HU J, SHEN L, SAMUEL A, SUN G, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[21] HOU Q B, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[22] ZHENG Z H, WANG P, REN D W, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 8574-8586.
[23] LI K, WAN G, CHENG G, et al. Object detection in optical remote sensing images: a survey and a new benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159: 296-307.
[24] QIAN W, YANG X, PENG S L, et al. Leaming modulated loss for rotated object detection[J]. arXiv:1911.08299, 2019.
[25] MING Q, ZHOU Z Q, MIAO L J, et al. Dynamic anchor learning for arbitrary-oriented object detection[J]. arXiv:2012. 04150, 2020.
[26] YANG X, YAN J C, FENG Z M, et al. R3Det: refined single-stage detector with feature refinement for rotating object[J]. arXiv:1908.05612, 2019.
[27] MING Q, MIAO L J, ZHOU Z Q, et al. CFC-Net: a critical feature capturing network for arbitrary-oriented object detection in remote-sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
[28] YI J R, WU P X, LIU B, et al. Oriented object detection in aerial images with box boundary-aware vectors[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021: 2149-2158.