Paper Info
Title
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.
Year
DOI
Venue
2021
10.1142/S0218126621502017
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
Keywords
DocType
Volume
Pillar number plate detection, pillar number plate recognition, attention mechanism, feature fusion, convolutional neural networks
Journal
30
Issue
ISSN
Citations 
11
0218-1266
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Shangdong Zheng100.34
Zebin Wu226030.82
Yang Xu371183.57
Zhihui Wei442850.68
Wei Xu5411.47
Jianxin Liu600.34
Daohua Ding700.34
Jiandong Yang801.35