Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detec...

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Main Authors: Lijing Wei, Cheng Xu, Siqi Li, Xiaohan Tu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9081973/
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author Lijing Wei
Cheng Xu
Siqi Li
Xiaohan Tu
author_facet Lijing Wei
Cheng Xu
Siqi Li
Xiaohan Tu
author_sort Lijing Wei
collection DOAJ
description Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods.
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spelling doaj.art-6c7a805c08e34e1c925dd0aded8333b02022-12-21T22:01:40ZengIEEEIEEE Access2169-35362020-01-018836118362110.1109/ACCESS.2020.29911959081973Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local FeaturesLijing Wei0https://orcid.org/0000-0001-9369-3114Cheng Xu1https://orcid.org/0000-0002-1323-3175Siqi Li2https://orcid.org/0000-0003-4807-3703Xiaohan Tu3https://orcid.org/0000-0002-4330-240XCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaTraffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods.https://ieeexplore.ieee.org/document/9081973/Traffic sign detectionmulti-scalecenter-point estimationlocal features
spellingShingle Lijing Wei
Cheng Xu
Siqi Li
Xiaohan Tu
Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
IEEE Access
Traffic sign detection
multi-scale
center-point estimation
local features
title Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
title_full Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
title_fullStr Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
title_full_unstemmed Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
title_short Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
title_sort traffic sign detection and recognition using novel center point estimation and local features
topic Traffic sign detection
multi-scale
center-point estimation
local features
url https://ieeexplore.ieee.org/document/9081973/
work_keys_str_mv AT lijingwei trafficsigndetectionandrecognitionusingnovelcenterpointestimationandlocalfeatures
AT chengxu trafficsigndetectionandrecognitionusingnovelcenterpointestimationandlocalfeatures
AT siqili trafficsigndetectionandrecognitionusingnovelcenterpointestimationandlocalfeatures
AT xiaohantu trafficsigndetectionandrecognitionusingnovelcenterpointestimationandlocalfeatures