关键词: 船舶视频检测, 深度学习, 背景建模, 卷积神经网络

Abstract: To avoid the excessive error during the implementation of background modeling and other traditional video object recognition algorithms in complex inland waterway environment, a new ship identification detection method based on faster region convolutional neural networks (i.e. Faster R-CNN) was proposed. The shortcomings of these traditional methods were analyzed, elaborating the mechanism of convolutional neural network and the subsequent regional convolutional neural network was also described. A model of R-CNN was put forward, which worked out how to construct and set the parameter of Loss Function, set region proposal networks (RPN) to predict a target boundary, and calculated the probability of matching targets. The actual video detection for moving inland river ships indicated that the ship identification algorithm held a ship recognition rate of over 90%. Meanwhile, this new algorithm had good robustness, 25.75% higher than traditional background modeling algorithm in situations with different visual clarity, from different perspectives, and regardless of the number of ships.

Key words: ship video recognition, deep learning, background modeling, convolutional neural networks