Multi-scale traffic sign detection model with attention

2020 ◽  
pp. 095440702095005
Author(s):  
Bei Bei Fan ◽  
He Yang
Keyword(s):  
Spatial Attention ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Background Information ◽  
Traffic Sign ◽  
Data Set ◽  
Multi Scale ◽  
Sign Detection ◽  
Object Area ◽  
Traffic Sign Detection

The current traffic sign detection technology is disturbed by factors such as illumination changes, weather, and camera angle, which makes it unsatisfactory for traffic sign detection. The traffic sign data set usually contains a large number of small objects, and the scale variance of the object is a huge challenge for traffic indication detection. In response to the above problems, a multi-scale traffic sign detection algorithm based on attention mechanism is proposed. The attention mechanism is composed of channel attention mechanism and spatial attention mechanism. By filtering the background information on redundant contradictions with channel attention mechanism in the network, the information on the network is more accurate, and the performance of the network to recognize the traffic signs is improved. Using spatial attention mechanism, the proposed method pays more attention to the object area in traffic recognition image and suppresses the non-object area or background areas. The model in this paper is validated on the Tsinghua-Tencent 100K data set, and the accuracy of the experiment reached a higher level compared to state-of-the-art approaches in 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.

Traffic sign detection algorithm based on improved Faster R-CNN

2021 ◽  
Vol 36 (3) ◽  
pp. 484-492
Author(s):  
Zhe LI ◽  
◽  
Hui-hui ZHANG ◽  
Jun-yong DENG
Keyword(s):  
Detection Algorithm ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

Algorithms ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 127 ◽  
Author(s):  
Jianming Zhang ◽  
Manting Huang ◽  
Xiaokang Jin ◽  
Xudong Li
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Research on Traffic Sign Detection Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3304-3308
Author(s):  
Mei Hua Xu ◽  
Yi Da Liu ◽  
Chen Jun Xia
Keyword(s):  
Clustering Algorithm ◽  
Color Image ◽  
Ccd Camera ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Image Separation ◽  
Series Of Experiments ◽  
Traffic Sign Detection

As an important part of Advanced Driver Assistance System (ADAS), the traffic sign detection has been paid more and more attention. This paper studied and implemented a valid algorithm of traffic sign detection. Using K-means clustering algorithm to complete the image separation and extraction of prohibition signs from the RGB color image, and then matching them with templates to realize the detection of traffic signs by SIFT algorithm. Series of experiments for traffic sign detection have been carried out to prove the validity and correctness of the algorithm on the basis of the road images in front of the vehicle collected by CCD camera.

scholarly journals Traffic sign Detection using CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 129-135
Author(s):  
K. Mirunalini ◽  
Vasantha Kalyani David
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Traffic Sign ◽  
Straight Line ◽  
Essential Components ◽  
Sign Detection ◽  
Important Challenge ◽  
Bad Weather ◽  
Traffic Sign Detection

Lane Detection and Traffic sign detection are the essential components in ADAS .Although there has been significant quantity of analysis dedicated to the detection of lane detection and sign detection in the past, there is still need robustness in the system. An important challenge in the current algorithm is to cope with the bad weather and illumination. In this paper proposes an improved Hough transform algorithm in order to achieve detection of straight line while for the detection of curved sections, the tracking algorithm is studied. The proposed method uses Hybrid KSVD for removing the noise and Hybrid Lane Detection Algorithm is used for identifying the lanes and CNN based approach is used for the Traffic sign Detection. The proposed method offers better Peak Signal to Noise Ratio (PSNR) and Root Mean Square (RMS) in contrast to the existing methods.

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