Pillar Number Plate Detection and Recognition in Unconstrained Scenarios

Abstract

Overhead catenary system (OCS) images exhibit great variations with clutter backgrounds, complex scenes and oblique views which pose great difficulty for automatic pillar number plate (NP) detection and recognition (NPDAR). Although these tasks have an important and practical significance for railway transportation, little researches have been done on these fields. In this paper, we propose a complete automatic NPDAR system with two main advantages: (1) For detection task, we propose Skip Connection Attention Module (SCAM) for adaptive feature refinement. Based on SCAM, the Attention_Guided Feature Fusion (AFF) module is designed for building high-level feature maps at different scales. (2) A novel convolution module, width/height convolution module (W/H-CM) was designed for NP recognition to capture global feature information efficiently. The W/H-CM extracts contextual information from two other perspectives compared to common convolution operation and iteratively generates supplementary information, making the representation of features more comprehensive. Both of them can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. We conduct extensive experiments on both our datasets and standard benchmarks PASCAL-VOC, MS COCO to verify competitive performance of our method.