Abstract
Based on the research and development of remote sensing image target extraction technology, the deep learning framework of cascade principal component analysis network is used to study the sea surface vessel detection algorithm. The visible image of the sea surface vessel is the input, the suspected target area is determined by the significance test, the PVANet model is extracted from the suspected target area, and the result is input into the support vector machine to obtain the final classification result. The experimental results show that the designed algorithm can successfully output the results of the detection of the sea area in the airspace, and verify the efficiency and accuracy of the PVANet model by comparing with the CNN algorithm. It proves the superiority of the PVANet model in feature extraction.
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Lv, J., Zhang, B., Li, X. (2019). Research on Object Detection Algorithm Based on PVANet. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_13
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DOI: https://doi.org/10.1007/978-981-13-0341-8_13
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