Contour Accentuation for Transfer Learning-Based Ship Recognition Method

Chi-Hua Chen, Yizhuo Zhang, Wenzhong Guo, Mingyang Pan, Lingjuan Lyu, Chia-Yu Lin

Abstract: This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.