Using Transferred Deep Model in Combination with Prior Features to Localize Multi-style Ship License Numbers in Nature Scenes

Baolong Liu, Xingzheng Lyu, Chao Li, Sanyuan Zhang, Zhenjie Hong, Xiuzi Ye

Abstract: Ship License Numbers (SLNs) localization is an important part of waterway intelligent transportation systems. Unfortunately, this issue has been neglected for a long time. In this paper, we present an effective approach for localizing multi-style SLNs in nature scenes. The problem of locating SLNs is posed as the detection of character sequences which possess SLNs prior features. First, faced with the difficulty of no training data, a transfer learning-based deep convolutional neural network is designed to detect character sequences in SLNs. In the second step, to accurately locate SLNs from the detected character sequences, the prior features of SLNs are considered. Three SLNs prior features are summarized. An SLNs region generating algorithm and a low-level similarity-based fake SLNs filtering algorithm are presented, respectively. The accurate positions of the SLNs in the input image are obtained in this stage. The proposed approach is finally tested on ZJUSHIPS950 dataset. The approach achieves a FPPI of 0.42 and a F-measure of 0.614 on 1374 labeled SLNs, surpassing several related methods by a large margin. Controlled experiment results also prove the impressive performances of the proposed SLNs region generating and fake SLNs filtering algorithms.