Traffic sign detection and recognition using fuzzy segmentation approach and artificial neural network classifier respectively

2017 ◽  
Author(s):  
Zainal Abedin ◽  
Prashengit Dhar ◽  
Muhammad Kamal Hossenand ◽  
Kaushik Deb
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Classifier ◽  
Traffic Sign ◽  
Artificial Neural Network Classifier ◽  
Sign Detection ◽  
Segmentation Approach ◽  
Fuzzy Segmentation ◽  
Traffic Sign Detection ◽  
Detection And Recognition

scholarly journals Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189855-189868
Author(s):  
Aleksej Avramovic ◽  
Davor Sluga ◽  
Domen Tabernik ◽  
Danijel Skocaj ◽  
Vladan Stojnic ◽  
...  
Keyword(s):  
Neural Network ◽  
High Definition ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

scholarly journals A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Haifeng Wan ◽  
Lei Gao ◽  
Manman Su ◽  
Qinglong You ◽  
Hui Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Extreme Conditions ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection is extremely important in autonomous driving and transportation safety systems. However, the accurate detection of traffic signs remains challenging, especially under extreme conditions. This paper proposes a novel model called Traffic Sign Yolo (TS-Yolo) based on the convolutional neural network to improve the detection and recognition accuracy of traffic signs, especially under low visibility and extremely restricted vision conditions. A copy-and-paste data augmentation method was used to build a large number of new samples based on existing traffic-sign datasets. Based on You Only Look Once (YoloV5), the mixed depth-wise convolution (MixConv) was employed to mix different kernel sizes in a single convolution operation, so that different patterns with various resolutions can be captured. Furthermore, the attentional feature fusion (AFF) module was integrated to fuse the features based on attention from same-layer to cross-layer scenarios, including short and long skip connections, and even performing the initial fusion with itself. The experimental results demonstrated that, using the YoloV5 dataset with augmentation, the precision was 71.92, which was increased by 34.56 compared with the data without augmentation, and the mean average precision mAP_0.5 was 80.05, which was increased by 33.11 compared with the data without augmentation. When MixConv and AFF were applied to the TS-Yolo model, the precision was 74.53 and 2.61 higher than that with data augmentation only, and the value of mAP_0.5 was 83.73 and 3.68 higher than that based on the YoloV5 dataset with augmentation only. Overall, the performance of the proposed method was competitive with the latest traffic sign detection approaches.

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