scholarly journals Improved YOLO v5 with balanced feature pyramid and attention module for traffic sign detection

2022 ◽  
Vol 355 ◽  
pp. 03023
Author(s):  
Linfeng Jiang ◽  
Hui Liu ◽  
Hong Zhu ◽  
Guangjian Zhang
Keyword(s):  
Feature Fusion ◽  
Detection Methods ◽  
Traffic Sign ◽  
Global Context ◽  
Automatic Driving ◽  
Computational Overhead ◽  
Pyramid Structure ◽  
Sign Detection ◽  
Feature Pyramid ◽  
Traffic Sign Detection

With the development of automatic driving technology, traffic sign detection has become a very important task. However, it is a challenging task because of the complex traffic sign scene and the small size of the target. In recent years, a number of convolutional neural network (CNN) based object detection methods have brought great progress to traffic sign detection. Considering the still high false detection rate, as well as the high time overhead and computational overhead, the effect is not satisfactory. Therefore, we employ lightweight network model YOLO v5 (You Only Look Once) as our work foundation. In this paper, we propose an improved YOLO v5 method by using balances feature pyramid structure and global context block to enhance the ability of feature fusion and feature extraction. To verify our proposed method, we have conducted a lot of comparative experiments on the challenging dataset Tsinghua-Tencent-100K (TT100K). The experimental results demonstrate that the [email protected] and [email protected]:0.95 are improved by 1.9% and 2.1%, respectively.

scholarly journals Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86578-86596 ◽  
Author(s):  
Chunsheng Liu ◽  
Shuang Li ◽  
Faliang Chang ◽  
Yinhai Wang
Keyword(s):  
Machine Vision ◽  
Detection Methods ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3192 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Ting Rui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Gabor Wavelets ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Driving ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition ◽  
Grayscale Images

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

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 for Intelligent Transportation Systems: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01006
Author(s):  
Ayoub Ellahyani ◽  
Ilyas El Jaafari ◽  
Said Charfi
Keyword(s):  
Intelligent Transportation Systems ◽  
Color Space ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Detection Methods ◽  
Data Sets ◽  
Traffic Sign ◽  
Current State ◽  
Sign Detection ◽  
Traffic Sign Detection

Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.

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