Abstract
Ship image target detection has important applications for ship management. In recent years, target detection based on deep learning has been widely studied in visual ship target detection. However, due to the difference and overlap of ship targets, the target loss rate is high. In order to solve the problem, this paper proposes a target detection algorithm based on improved RetinaNet for ship image target detection. The cyclical focal loss function and CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively increases ship detection precision by 2.5%.
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Sun, Y., Fan, T. (2023). Ship Target Detection in Remote Sensing Image Based on Improved RetinaNet. In: Patnaik, S., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 349. Springer, Singapore. https://doi.org/10.1007/978-981-99-1230-8_10
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DOI: https://doi.org/10.1007/978-981-99-1230-8_10
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