Abstract
The object detection in the maritime field is becoming more important thanks to the different offers it provides, such as: monitoring, traffic management, coastal control, etc. However, the maritime environment is known for its complexity, which poses difficulties in detecting targets, especially with the classical method. Hence the importance of introducing an intelligence layer to the acquisition data that will support the decision support part and facilitate the detection of an undesirable event. This paper aims to make video surveillance in the maritime domain intelligent through the use of the advantages of machine learning, and more specifically the implementation of the YOLOv7 model that will allow us to provide real-time detection, rigorous precision of the different types of vessels, plus the speed of processing of frames in the configuration used. The experimental results prove that the model YOLOV7 as the latest version of yolo is the model that gives the most efficient results when compared with the other existing models.
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References
Z. Shao, L. Wang, Z. Wang, W. Du, W. Wu, Saliency-aware convolution neural network for ship detection in surveillance video. IEEE Trans. Circuits Syst. Video Technol. 30(13). 781–794 (2019)
Z. Zou, Z. Shi, Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens. 10(154), 5832–5845(2016)
Z. Shi, X. Yu, Z. Jiang, B. Li, Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature. IEEE Trans. Geosci. Remote Sens. 8(152):4511–4523 (2013)
J. Tang, C. Deng, G. Huang, B. Zhao, Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 3(153):1174–1185 (2014)
K. Kim, S. Hong, B. Choi, E. Kim, Probabilistic ship detection and classification using deep learning. Appl. Sci. 6(18), 936 (2018)
N. Wawrzyniak, T. Hyla, A. Popik, Vessel detection and tracking method based on video surveillance. Sensors 23(119), 5230 (2019)
H. Li, L. Deng, G. Yang, J. Liu, Z. Gu, Enhanced YOLO v3 tiny network for real-time ship detection from visual image. IEEE Access 9, 16692–16706 (2021)
H. Fu, G. Song, Y. Wang, Improved YOLOv4 marine target detection combined with CBAM. Symmetry 13(14), 623 (2021)
C.Y. Wang, A. Bochkovskiy, H.Y.M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv. 2207.02696 (2022)
J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271 (2017)
W. Liu, D. Anguelov, D. Erhan, S. Christian, S. Reed, C.-Y. Fu, C.B. Alexander, SSD: Single Shot MultiBox Detector (Springer, Cham, 2016), pp. 21–37
P.D. Hung, N. Kien, Ngoc: SSD-Mobilenet Implementation for Classifying Fish Species (Springer, Cham, 2019), pp. 399–408
R. Girshick, Fast r-cnn. Proceedings of the IEEE international conference on computer vision, 1440–1448 (2015)
A.O. Vuola, S.U. Akram, J. Kannala, Mask-RCNN and U-net Ensembled for Nuclei Segmentation (IEEE, 2019), pp. 208–212
J. Redmon, A. Farhadi, YOLOv3: an incremental improvement. arXiv. 1804.02767 (2018)
A. Bochkovskiy, C.Y. Wang, H.Y.M. Liao, YOLOv4: Optimal speed and accuracy of object detection. arXiv. 2004.10934 (2020)
Y. Zhao, Y. Shi, Z. Wang, The Improved YOLOV5 Algorithm and Its Application in Small Target Detection (Springer, Cham, 2022), pp. 679–688
Z. Shao, W. Wu, Z. Wang, S. Zhenfeng Shao, W. Du, C. Li, SeaShips: a large-scale precisely annotated dataset for ship detection. IEEE Trans Multimed 20(110), 2593–2604 (2018)
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Haijoub, A., Hatim, A., Arioua, M., Hammia, S., Eloualkadi, A., Guerrero-González, A. (2023). Fast Yolo V7 Based CNN for Video Streaming Sea Ship Recognition and Sea Surveillance. In: Idrissi, A. (eds) Modern Artificial Intelligence and Data Science. Studies in Computational Intelligence, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-031-33309-5_8
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DOI: https://doi.org/10.1007/978-3-031-33309-5_8
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