A Two-Stage Approach for Traffic Sign Detection with Arrow Signal Recognition Using Convolutional Neural Networks

2021 ◽  
pp. 149-164
Author(s):  
Tien-Wen Yeh ◽  
Huei-Yung Lin ◽  
Mu-Yun Tsai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Signal Recognition ◽  
Traffic Sign ◽  
Two Stage ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification

2020 ◽  
Vol 14 (12) ◽  
pp. 1712-1722
Author(s):  
Zhanwen Liu ◽  
Chao Shen ◽  
Xing Fan ◽  
Gaowen Zeng ◽  
Xiangmo Zhao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Enhancing the robustness of the convolutional neural networks for traffic sign detection

2021 ◽  
pp. 095440702110429
Author(s):  
Amir Khosravian ◽  
Abdollah Amirkhani ◽  
Masoud Masih-Tehrani
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Noisy Data ◽  
Climatic Conditions ◽  
Noisy Environments ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Model Robustness

The detection of traffic signs in clean and noise-free images has been investigated by numerous researchers; however, very few of these works have focused on noisy environments. While in the real world, for different reasons (e.g. the speed and acceleration of a vehicle and the roughness around it), the input images of the convolutional neural networks (CNNs) could be extremely noisy. Contrary to other research works, in this paper, we investigate the robustness of the deep learning models against the synthetically modeled noises in the detection of small objects. To this end, the state-of-the-art architectures of Faster-RCNN Resnet101, R-FCN Resnet101, and Faster-RCNN Inception Resnet V2 are trained by means of the Tsinghua-Tencent 100K database, and the performances of the trained models on noisy data are evaluated. After verifying the robustness of these models, different training scenarios (1 – Modeling various climatic conditions, 2 – Style randomization, and 3 – Augmix augmentation) are used to enhance the model robustness. The findings indicate that these scenarios result in up to 13.09%, 12%, and 13.61% gains in the mentioned three networks by means of the mPC metric. They also result in 11.74%, 8.89%, and 7.27% gains in the rPC metric, demonstrating that improvement in robustness does not lead to performance drop on the clean data.

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