Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

• Autorzy: Gajjar Pranshav , Garg Manav , Shah Vatsal , Shah Pooja , Das Anup

Identyfikatory

DOI

10.2478/rgg-2022-0005

• Języki publikacji: EN

Abstrakty

EN

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.

Słowa kluczowe

EN

deep learning; attention; ship detection; U-Net

PL

uczenie głębokie; uwaga; wykrywanie statków; U-Net

• Wydawca: Wydział Geodezji i Kartografii Politechniki Warszawskiej
• Czasopismo: Reports on Geodesy and Geoinformatics
• Rocznik: 2022
• Tom: Vol. 114
• Strony: 9--14
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Gajjar Pranshav

• Department of Computer Science and Engineering, Institute of Technology, Nirma Univeristy

• https://orcid.org/0000-0002-1730-9640

autor

Garg Manav

• Department of Computer Science and Engineering, Institute of Technology, Nirma Univeristy

• https://orcid.org/0000-0002-5979-2079

autor

Shah Vatsal

• Department of Computer Science and Engineering, Institute of Technology, Nirma Univeristy

autor

Shah Pooja

• Department of Computer Science and Engineering, Institute of Technology, Nirma Univeristy

autor

Das Anup

• Space Application Centre, Indian Space Research Organisation

Bibliografia

• [1] Bianchi, F. M., Espeseth, M. M., and Borch, N. (2020). Large-scale detection and categorization of oil spills from SAR images with deep learning.10.3390/rs12142260
• [2] Coman, C. and Thaens, R. (2018). A deep learning SAR target classification experiment on MSTAR dataset. In 2018 19th International Radar Symposium (IRS). IEEE, doi:10.23919/irs.2018.8448048.10.23919/IRS.2018.8448048
• [3] Ding, Y., Chen, F., Zhao, Y., Wu, Z., Zhang, C., and Wu, D. (2019). A stacked multi-connection simple reducing net for brain tumor segmentation. IEEE Access, 7:104011–104024, doi:10.1109/access.2019.2926448.10.1109/ACCESS.2019.2926448
• [4] Gajjar, P., Shah, P., Vegada, A., and Savalia, J. (2022). Triplet loss for chromosome classification. Journal of Innovative Image Processing, 4(1):1–15, doi:10.36548/jiip.2022.1.001.10.36548/jiip.2022.1.001
• [5] He, Y. and Wang, S. (2022). SE-BLTCNN: A channel attention adapted deep learning model based on PSSM for membrane protein classification. Computational biology and chemistry, 98:107680, 35421797, doi:10.1016/j.compbiolchem.2022.107680.10.1016/j.compbiolchem.2022.10768035421797
• [6] Holloway, J. and Mengersen, K. (2018). Statistical machine learning methods and remote sensing for sustainable development goals: A review. Remote Sensing, 10(9):1365, doi:10.3390/rs10091365.10.3390/rs10091365
• [7] Huang, G., Wang, Y., Zhang, Y., and Tian, Y. (2011). Ship detection using texture statistics from optical satellite images. In 2011 International Conference on Digital Image Computing: Techniques and Applications. IEEE, doi:10.1109/dicta.2011.91.10.1109/DICTA.2011.91
• [8] Karki, S. and Kulkarni, S. (2021). Ship detection and segmentation using Unet. In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, doi:10.1109/icaect49130.2021.9392463.10.1109/ICAECT49130.2021.9392463
• [9] Khan, H. M. and Yunze, C. (2018). Ship detection in SAR image using yolov2. In 2018 37th Chinese Control Conference (CCC). IEEE, doi:10.23919/chicc.2018.8482863.10.23919/ChiCC.2018.8482863
• [10] Khan, R. A., Luo, Y., and Wu, F.-X. (2022). RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation. Artificial Intelligence in Medicine, 124:102231, doi:10.1016/j.artmed.2021.102231.10.1016/j.artmed.2021.10223135115126
• [11] Li, J., Qu, C., and Shao, J. (2017a). Ship detection in SAR images based on an improved faster R-CNN. In 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA). IEEE, doi:10.1109/bigsardata.2017.8124934.10.1109/BIGSARDATA.2017.8124934
• [12] Li, W., Fu, K., Sun, H., Sun, X., Guo, Z., Yan, M., and Zheng, X. (2017b). Integrated localization and recognition for inshore ships in large scene remote sensing images. IEEE Geoscience and Remote Sensing Letters, 14(6):936–940, doi:10.1109/lgrs.2017.2688357.10.1109/LGRS.2017.2688357
• [13] Mehta, N., Shah, P., and Gajjar, P. (2021). Oil spill detection over ocean surface using deep learning: a comparative study. Marine Systems & Ocean Technology, 16(3-4):213–220, doi:10.1007/s40868-021-00109-4.10.1007/s40868-021-00109-4
• [14] Mehta, N., Shah, P., Gajjar, P., and Ukani, V. (2022). Ocean surface pollution detection: Applicability analysis of V-Net with data augmentation for oil spill and other related ocean surface feature monitoring. In Communication and Intelligent Systems, pages 11–25. Springer.10.1007/978-981-19-2130-8_2
• [15] Morillas, J. R. A., Garcia, I. C., and Zolzer, U. (2015). Ship detection based on SVM using color and texture features. In 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, doi:10.1109/iccp.2015.7312682.10.1109/ICCP.2015.7312682
• [16] Niu, J.-Y., Xie, Z.-H., Li, Y., Cheng, S.-J., and Fan, J.-W. (2021). Scale fusion light CNN for hyperspectral face recognition with knowledge distillation and attention mechanism. Applied Intelligence, 52(6):6181–6195, doi:10.1007/s10489-021-02721-8.10.1007/s10489-021-02721-8
• [17] Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Lecture Notes in Computer Science, pages 234–241. Springer International Publishing, doi:10.1007/978-3-319-24574-4_28.10.1007/978-3-319-24574-4_28
• [18] Shamsolmoali, P., Chanussot, J., Zareapoor, M., Zhou, H., and Yang, J. (2021). Multipatch feature pyramid network for weakly supervised object detection in optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60:1–13.10.1109/TGRS.2021.3106442
• [19] Shan, W., Wang, Y., and Lu, W. (2021). ECA-UNet: Denoise seismic data by learning from traditional method. In First International Meeting for Applied Geoscience & Energy Expanded Abstracts. Society of Exploration Geophysicists, doi:10.1190/segam2021-3583394.1.10.1190/segam2021-3583394.1
• [20] Trappenberg, T. P. (2019). Machine learning with sklearn. In Fundamentals of Machine Learning, pages 38–65. Oxford University Press, doi:10.1093/oso/9780198828044.003.0003.10.1093/oso/9780198828044.003.0003
• [21] Trebing, K., Stanczyk, T., and Mehrkanoon, S. (2021). SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture. Pattern Recognition Letters, 145:178–186, doi:10.1016/j.patrec.2021.01.036.10.1016/j.patrec.2021.01.036
• [22] Wang, J., Yu, Z., Luan, Z., Ren, J., Zhao, Y., and Yu, G. (2022). RDAUNet: Based on a residual convolutional neural network with DFP and CBAM for brain tumor segmentation. Frontiers in Oncology, 12, doi:10.3389/fonc.2022.805263.10.3389/fonc.2022.805263892461135311076
• [23] Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020). ECA-Net: Efficient channel attention for deep convolutional neural networks. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, doi:10.1109/cvpr42600.2020.01155.10.1109/CVPR42600.2020.01155
• [24] Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S. (2018). CBAM: convolutional block attention module. In Computer Vision – ECCV 2018, pages 3–19. Springer International Publishing, doi:10.1007/978-3-030-01234-2_1.10.1007/978-3-030-01234-2_1
• [25] Yang, F., Xu, Q., Gao, F., and Hu, L. (2015). Ship detection from optical satellite images based on visual search mechanism. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, doi:10.1109/igarss.2015.7326621.10.1109/IGARSS.2015.7326621
• [26] Yu, G. and Sapiro, G. (2011). DCT image denoising: a simple and effective image denoising algorithm. Image Processing On Line, 1:292–296, doi:10.5201/ipol.2011.ys-dct.10.5201/ipol.2011.ys-dct
• [27] Zareapoor, M., Chanussot, J., Zhou, H., Yang, J., et al. (2021). Rotation equivariant feature image pyramid network for object detection in optical remote sensing imagery.
• [28] Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L. (2016). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. doi:10.1109/TIP.2017.2662206.10.1109/TIP.2017.266220628166495
• [29] Zhang, Z., Liu, Q., and Wang, Y. (2018). Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5):749–753, doi:10.1109/lgrs.2018.2802944.10.1109/LGRS.2018.2802944
• [30] Zhao, Z., Chen, K., and Yamane, S. (2021). CBAM-Unet++:easier to find the target with the attention module “CBAM”. In 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). IEEE, doi:10.1109/gcce53005.2021.9622008.10.1109/GCCE53005.2021.9622008
• [31] Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J. (2020). UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6):1856–1867, doi:10.1109/tmi.2019.2959609.10.1109/TMI.2019.2959609735729931841402
• [32] Zhu, C., Zhou, H., Wang, R., and Guo, J. (2010). A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Transactions on Geoscience and Remote Sensing, 48(9):3446–3456, doi:10.1109/tgrs.2010.2046330.10.1109/TGRS.2010.2046330

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).