Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.

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An Approach of Traffic Sign Recognition Algorithm on MATLAB

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.3980 ◽

2014 ◽

Vol 644-650 ◽

pp. 3980-3983

Author(s):

Jia Yang Li ◽

Mei Xia Song

Keyword(s):

Real Time ◽

Intelligent Transportation System ◽

Recognition System ◽

Recognition Algorithm ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Sign Recognition ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

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Hardware Efficient Modified CNN Architecture for Traffic Sign Detection and Recognition

International Journal of Image and Graphics ◽

10.1142/s0219467822500176 ◽

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.

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A Review and Comparative Analysis of Recent Advancements in Traffic Sign Detection and Recognition Techniques

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v2i1.1594 ◽

2015 ◽

Vol 2 (1) ◽

Author(s):

Khyati Chourasia ◽

Jitendra N. Chourasia

Keyword(s):

Template Matching ◽

Safety Information ◽

Recognition System ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Fuzzy Classifier ◽

Sign Recognition ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

This paper presents a comprehensive study of the automatic detection and recognition of traffic sign. The object of this review is to reduce the search for quality Traffic sign recognition system and to indicate the potential regions for increasing the efficiency, accuracy and speed of the system. The traffic sign carry the very important and valuable safety information through the peculiar characteristics. Different categories of traffic sign with their characteristics are presented. The practical difficulty that arises in actual time traffic sign is summarized. It describes also the techniques used for the detection, recognition and classification of the traffic signs. The traffic sign detection using color and shape detection are most commonly used. Some authors also used adaboost detector and decision tree method for detection. Most of the researcher used different type of Neural Network for recognition and classification. Some of the authors used fuzzy classifier and genetic algorithm. Template matching and model based method is also used for classification. A lot of improvements are still required for development efficient, fast, robustness traffic sign recognition system.

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New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Sensors ◽

10.3390/s18113776 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3776 ◽

Cited By ~ 2

Author(s):

Jameel Khan ◽

Donghoon Yeo ◽

Hyunchul Shin

Keyword(s):

Recognition Performance ◽

Recognition System ◽

Tone Mapping ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Traffic Signs ◽

Sign Recognition ◽

Sign Detection ◽

Illumination Preprocessing ◽

Traffic Sign Detection

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

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Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Sensors ◽

10.3390/s19184021 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4021 ◽

Cited By ~ 8

Author(s):

Jingwei Cao ◽

Chuanxue Song ◽

Silun Peng ◽

Feng Xiao ◽

Shixin Song

Keyword(s):

Recognition Rate ◽

Recognition Algorithm ◽

Intelligent Vehicles ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Traffic Signs ◽

Sign Recognition ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

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

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190823112357 ◽

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.

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