Abstract
When detecting sea-sky line (SSL) in the infrared image, the blurry SSL, conspicuous sea clutter affects the accurate detection of SSL seriously. To solve these problems, we proposed a robust SSL detection algorithm based on the vertical grayscale distribution feature (VGDF). We divided the infrared image into sub-image blocks by sliding window. The sub-image blocks that contain SSL in the central area are labeled as positive samples, and those without any SSL are labeled as negative samples. To improve the separability of the samples, the vertical grayscale distribution feature map (VGDF map) transformation method is proposed to transform the gray sub-image blocks into the feature maps. The VGDF maps are used as the input of the convolutional neural network to train the SSL recognition model. This strategy can improve the separability of SSL image blocks from background image blocks. Then, we use the trained model to obtain the edge candidates and construct the SSL probability feature map. Finally, we detect the SSL by fitting a straight line with the greatest probability on the SSL probability feature map. The proposed algorithm realized 99.4% accuracy rate on the dataset containing 1320 frames of infrared images. The comparison results showed that our algorithm obtained higher detection accuracy than the existing state-of-the-art algorithms. Our algorithm performs well even when the SSL was blurred or there are obvious ship’s wave wakes on the sea surface.
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References
Lin, R., Yuan, W., Chen, X.: An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system. Ocean Eng. 235, 109435 (2021)
Zhang, W., He, X., Li, W.: An integrated ship segmentation method based on discriminator and extractor. Image Vis. Comput. 93, 1–12 (2020)
Liu, Z., Bai, X., Sun, C.: Infrared ship target segmentation through integration of multiple feature maps. Image Vis. Comput. 48, 14–25 (2016). https://doi.org/10.1016/j.imavis.2015.12.005
Zhou, A.R., **e, W.X., Pei, J.H.: Infrared maritime target detection using the high order statistic filtering in fractional Fourier domain. Inf. Phys. & Techno. 91, 123–136 (2018)
Fu, J., Zhao, J.H., Li, F.: Infrared sea-sky line detection utilizing self-adaptive laplacian of gaussian filter and visual-saliency-based probabilistic Hough transform. IEEE Geosci. Rem. Sens. Lett. (2021). https://doi.org/10.1109/LGRS.2021.3111099
Liang, D., Liang, Y.: Horizon detection from electro-optical sensors under maritime environment. IEEE Trans.Instrum. Measurement. 69(1), 45–53 (2020)
Ma, D., Dong, L.L., Xu, W.: A method for infrared sea-sky condition judgment and search system: robust target detection via PLS and CEDoG. IEEE Access. 9, 1439–1453 (2021)
Feng, T., Liu, J., **ao, J.: Sea-sky line detection method: an overview. Laser & Optoelectron. Prog. 57(16), 1–21 (2020)
Lin, C., Chen, W., Zhou, H.: Multi-visual feature saliency detection for sea-surface targets through improved sea-sky-line detection. J. Mar. Sci Eng. (2020). https://doi.org/10.3390/jmse8100799
Zardoua, Y., Astito, A., Boulaala, M.: A survey on horizon detection algorithms for maritime video surveillance: advances and future techniques. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02321-0
Ben, F.H., Bouguezzi, S., Souani, C.: Face recognition in unconstrained environment with CNN. Vis. Comput. 37, 217–226 (2021). https://doi.org/10.1007/s00371-020-01794-9
Zhang, L., Yan, L., Zhang, M.: T2CNN: a novel method for crowd counting via two-task convolutional neural network. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02313-0
Li, F., Zhang, J., Sun, W.: Sea-sky line detection using gray variation differences in the time domain for unmanned surface vehicles. Signal Image Video Process 15, 139–146 (2021)
Zafarifar, B., Weda, H. and De, P.H.N.: Horizon detection based on sky-color and edge features. In: Proceedings-SPIE the International Society for Optical Engineering, 6822(2), 6822-29 (2008)
Mo, W.Y., and Pei, J.H.: Sea Surface Boundary Line Detection Based on Edge Energy Feature Matrix of Sequential Images. In: IEEE Conference IAEAC, pp. 1397–1401 (2019)
Qiu, R., Lu, J., Gong, J.: Research on general detection method of coastline and sea-sky line in FLIR image. Acta Armamentarii. 40(6), 1171–1178 (2019)
Shao, X.H., Pei, J.H., Zhao, Y.: Sea-sky line detection based on boundary prior double model Bayesian decision in infrared image. Signal Process 35(05), 877–887 (2019)
Wang, B., Su, Y., Wan, L.: A sea-sky line detection method for unmanned surface vehicles based on gradient saliency. Sensors. 16(543), 1–18 (2016)
Yang, W., Li, H., Liu, J.: A sea-sky line detection method based on Gaussian mixture models and image texture features. Int. J. Adv. Rob. Syst. (2019). https://doi.org/10.1177/1729881419892116
Sun, X., Xu, Q., Cai, Y.: Sea sky line detection based on edge phase encoding in complicated background. Acta Opt. Sin. 37(11), 94–102 (2017)
Zhang, Z., Wang, Q., Zhu, X.: Sea-sky line detection method based on scene division. J. Huazhong Univ. Sci. Technol. Nat. Sci. Ed. 48(8), 1–8 (2020)
Liu, X., Zhao, C., Zhang, S.: A 2-layered structure sea- sky-line detection algorithm based on regional optimal variance. In: IEEE 13th International Conference on Electronic Measurement & Instruments(ICEMI), pp. 486–490 (2017)
Ma, T. and Ma, J.: A sea-sky line detection method based on line segment detector and hough transform. In: 2nd IEEE International Conference on Computer and Communication(ICCC). pp. 700–703 (2016)
Zhang, X., Ma, H., Li, Y.: Sea-sky line detection of UUV near surface infrared images based on the row-mean value gradient method and linear fitting algorithm. Appl. Sci. Technol. 45(04), 6–12 (2018)
Dai, Y., Liu, B., Li, L.: Sea-sky line detection based on local Otsu segmentation and Hough transform. Opto-Electron. Eng. 45(7), 1–9 (2018)
Zhan, W., Qiu, R., Ma, X.: Application of LSD and clustering in sea-sky line and coastline detection algorithm. Electron. Opt. Control. 26(1), 43–46 (2019)
Song, H., Ren, H., Song, Y.: A sea-sky line detection method based on the RANSAC algorithm in the background of infrared sea-land-sky images. Russ. Laser Res. 42, 318–327 (2021)
Zhang, Y., Li, Q., Zang, F.: Ship detection for visual maritime surveillance from non stationary platforms. Ocean Eng. 141, 53–63 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Redmon, J. and Farhadi, A.: YOLO9000: Better, Faster, Stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)
Zhao, K., Han, Q., Zhang, C.: Deep hough transform for semantic line detection. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3077129
Palmer, P., Kittler, J., Petrou, M.: Methods for improving line parameter accuracy in a Hough transform algorithm. In: IEE Colloquium on Hough Transforms. 31–33 (1993)
Xu, Z., Shin, B., Klette, R.: Accurate and robust line segment extraction using minimum entropy with hough transform. IEEE Trans. Image Process. 24(3), 813–822 (2015)
Suhr, J.K., Jung, H.G.: Dense stereo-based robust vertical road profile estimation using hough transform and dynamic programming. IEEE Trans. Intell. Transp. Syst. 16(3), 1528–1536 (2015)
Tschopp, F.: Hough2 map-iterative event-based hough transform for high-speed railway map**. IEEE Robot. Autom. Lett.. 6(2), 2745–2752 (2021)
Beeravolu, A.R., Azam, S., Jonkman, M.: Preprocessing of breast cancer images to create datasets for deep-CNN. IEEE Access 9, 33438–33463 (2021)
Bnouni, N., Amor, H., Rekik, I.: Boosting CNN learning by ensemble image preprocessing methods for cervical cancer segmentation. In: 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 264–269 (2021)
Prasad, D.K., Rajan, D., Rachmawati, L., Rajabaly, E., Quek, C.: Video processing from electro-optical sensors for object detection and tracking in maritime environment: a survey. IEEE Trans. Intell. Transp. Syst. 18(8), 1993–2016 (2017)
Jeong, C.Y., Yang, H.S., Moon, K.D.: A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection. Multidimension. Syst. Signal Process. 30, 1187–1204 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant (62071303, 61871269), Guangdong Basic and Applied Basic Research Foundation (2019A1515011861), and Shenzhen Science and Technology Projection (JCYJ20190808151615540).
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Mo, W., Pei, J. Sea-sky line detection in the infrared image based on the vertical grayscale distribution feature . Vis Comput 39, 1915–1927 (2023). https://doi.org/10.1007/s00371-022-02455-9
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DOI: https://doi.org/10.1007/s00371-022-02455-9