Abstract
A ship detection model based on Faster R-CNN is proposed for ship detection tasks in optical remote sensing images. Deep convolutional neural network could replace traditional manual design feature to extract ship features automatically and quickly from makes the detection performance of ship no longer relying on the design of artificial features. This paper proposes a strategy that combines the model with two different size of convolution neural networks respectively. Experiments on datasets HRSC16 verify the models detection capabilities and the mean average precision can achieve 78.2%. For the problem of low recall rate in the detection of adjacent vessels, this paper adopts the Soft-NMS method. Compared with the traditional NMS, the Soft-NMS method can electively improve the model detection performance to 80.1%. At the same time, it also shows that the model we proposed is a robust model and has a certain degree of generalization ability.
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Zhai, M., Liu, H., Sun, F., Zhang, Y. (2020). Ship Detection Based on Faster R-CNN Network in Optical Remote Sensing Images. In: Deng, Z. (eds) Proceedings of 2019 Chinese Intelligent Automation Conference. CIAC 2019. Lecture Notes in Electrical Engineering, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-32-9050-1_3
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DOI: https://doi.org/10.1007/978-981-32-9050-1_3
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