scholarly journals Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 112
Author(s):  
Shangwang Liu ◽  
Tongbo Cai ◽  
Xiufang Tang ◽  
Yangyang Zhang ◽  
Changgeng Wang
Keyword(s):  
Visual Recognition ◽  
Detection Method ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
Traffic Sign ◽  
Backbone Network ◽  
Network Training ◽  
Sign Detection ◽  
Traffic Sign Detection

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry

2019 ◽  
Vol 94 ◽  
pp. 381-391 ◽  
Author(s):  
Xianghua Xu ◽  
Jiancheng Jin ◽  
Shanqing Zhang ◽  
Lingjun Zhang ◽  
Shiliang Pu ◽  
...  
Keyword(s):  
Detection Method ◽  
Data Driven ◽  
Traffic Sign ◽  
Sign Detection ◽  
Smart Data ◽  
Traffic Sign Detection

An Efficient Small Traffic Sign Detection Method Based on YOLOv3

2020 ◽  
Author(s):  
Jixiang Wan ◽  
Wei Ding ◽  
Hanlin Zhu ◽  
Ming Xia ◽  
Zunkai Huang ◽  
...  
Keyword(s):  
Detection Method ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Hardware Efficient Modified CNN Architecture for Traffic Sign Detection and Recognition

2021 ◽  
pp. 2250017
Author(s):  
Bhaumik Vaidya ◽  
Chirag Paunwala
Keyword(s):  
Feature Fusion ◽  
Detection Accuracy ◽  
Field Calculation ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Adverse Weather ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic sign recognition is a vital part for any driver assistance system which can help in making complex driving decision based on the detected traffic signs. Traffic sign detection (TSD) is essential in adverse weather conditions or when the vehicle is being driven on the hilly roads. Traffic sign recognition is a complex computer vision problem as generally the signs occupy a very small portion of the entire image. A lot of research is going on to solve this issue accurately but still it has not been solved till the satisfactory performance. The goal of this paper is to propose a deep learning architecture which can be deployed on embedded platforms for driver assistant system with limited memory and computing resources without sacrificing on detection accuracy. The architecture uses various architectural modification to the well-known Convolutional Neural Network (CNN) architecture for object detection. It uses a trainable Color Transformer Network (CTN) with the existing CNN architecture for making the system invariant to illumination and light changes. The architecture uses feature fusion module for detecting small traffic signs accurately. In the proposed work, receptive field calculation is used for choosing the number of convolutional layer for prediction and the right scales for default bounding boxes. The architecture is deployed on Jetson Nano GPU Embedded development board for performance evaluation at the edge and it has been tested on well-known German Traffic Sign Detection Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset. The architecture only requires 11 MB for storage which is almost ten times better than the previous architectures. The architecture has one sixth parameters than the best performing architecture and 50 times less floating point operations per second (FLOPs). The architecture achieves running time of 220[Formula: see text]ms on desktop GPU and 578 ms on Jetson Nano which is also better compared to other similar implementation. It also achieves comparable accuracy in terms of mean average precision (mAP) for both the datasets.

scholarly journals Traffic sign detection method based on Faster R-CNN

2019 ◽  
Vol 1176 ◽  
pp. 032045 ◽  
Author(s):  
Linxiu Wu ◽  
Houjie Li ◽  
Jianjun He ◽  
Xuan Chen
Keyword(s):  
Detection Method ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection
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