Abstract
Collision between ships and icebergs is a major problem in glacial area, where large to small icebergs becomes a threat to cargo ships, tankers, fishing ships etc. In this paper, we have devised a new approach for the detection of icebergs and movement of ships to predict their probability of collision. In this proposed work, an adaptive method is used to detect the presence of icebergs and the velocity of ships, followed by integrating the obtained data and applying the Bayesian algorithm we have successfully computed the collision probability. This work exhibits effective results against reduced visibility due to fog. Besides, we have acquired all the foreground authentic data from valid resources. So, the results will help in marking the safe and unsafe zones in the form of clusters by using DBSCAN algorithm.
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Zishan Ali, S., Makhija, M., Choudhary, D., Singh, H. (2019). An Efficient and Adaptive Method for Collision Probability of Ships, Icebergs Using CNN and DBSCAN Clustering Algorithm. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_3
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DOI: https://doi.org/10.1007/978-981-13-8300-7_3
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