Research on Ocean Feature Mining Processing Based on Deep Convolution Optimization Algorithm

ABSTRACT Yang, S.; Chong, X.; Yan, Z.; Sun, J., and Fu, F., 2020. Research on ocean feature mining processing based on deep convolution optimization algorithm. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 58–62. Coconut Creek (Florida), ISSN 0749-0208. With the breakthrough progress of deep learning methods in ocean image classification and target detection, the sea-level tracking algorithms based on deep learning have also attracted widespread attention. Therefore, an anti-occlusion real-time target tracking algorithm using a deep convolution model to extract ocean image features is proposed in which offline pretraining is performed on a large-scale data set with a stack-type noise reduction auto-encoder to obtain general object representation capabilities Then, the particle filtering framework is introduced to input the labeled samples of the first frame in tracking data set for online fine-tuning so that the extracted features of different convolutional layers in the pretrained deep network can be combined with the relevant filtering framework. The algorithm in the paper cannot achieve only good tracking accuracy, but it also realized faster tracking speed under complex sea-level environments. The problem of target occlusion during tracking is solved as well.