Road Traffic Sign Detection Method Based on RTS R-CNN Instance Segmentation Network

Author:

Zhang Guirong1ORCID,Peng Yiming1,Wang Hai1ORCID

Affiliation:

1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Abstract

With the rapid development of the autonomous driving industry, there is increasing research on related perception tasks. However, research on road surface traffic sign detection tasks is still limited. There are two main challenges to this task. First, when the target object’s pixel ratio is small, the detection accuracy often decreases. Second, the existing publicly available road surface traffic sign datasets have limited image data. To address these issues, this paper proposes a new instance segmentation network, RTS R-CNN, for road surface traffic sign detection tasks based on Mask R-CNN. The network can accurately perceive road surface traffic signs and provide important information for the autonomous driving decision-making system. Specifically, CSPDarkNet53_ECA is proposed in the feature extraction stage to enhance the performance of deep convolutional networks by increasing inter-channel interactions. Second, to improve the network’s detection accuracy for small target objects, GR-PAFPN is proposed in the feature fusion part, which uses a residual feature enhancement module (RFA) and atrous spatial pyramid pooling (ASPP) to optimize PAFPN and introduces a balanced feature pyramid module (BFP) to handle the imbalanced feature information at different resolutions. Finally, data augmentation is used to generate more data and prevent overfitting in specific scenarios. The proposed method has been tested on the open-source dataset Ceymo, achieving a Macro F1-score of 87.56%, which is 2.3% higher than the baseline method, while the inference speed reaches 23.5 FPS.

Funder

National Natural Science Foundation of China

Key Research and Development Program of Jiangsu Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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